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X-WR-CALNAME:Robotics Institute Carnegie Mellon University
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X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260630T093000
DTEND;TZID=America/New_York:20260630T110000
DTSTAMP:20260707T193018
CREATED:20260622T175815Z
LAST-MODIFIED:20260622T175815Z
UID:151640-1782811800-1782817200@www.ri.cmu.edu
SUMMARY:Spatial Reasoning and Semantic Representations for Autonomous Exploration and Object Search
DESCRIPTION:Abstract: Autonomous robot exploration and object search in unknown environments are fundamental capabilities in robotics\, with applications ranging from search and rescue to structural inspection. A central challenge in both tasks is that robots often must make decisions based on information they have not yet directly observed–reasoning about unexplored space\, predicting future information gain\, or inferring where a target object is likely to be found. This thesis argues that autonomous robots can achieve efficient exploration and long-horizon object search by leveraging spatial and semantic representations of increasing expressiveness that enable reasoning beyond immediate sensor observations. \nWe develop this argument through a progression of representations. We begin by extracting geometric cues–such as rooms\, doorways\, and spatial connectivity–directly from sensor data\, and show how a team of robots can use these compact structural representations to coordinate room-based exploration in indoor environments. We then move beyond what has already been observed: by learning to predict unobserved portions of an environment\, a robot can estimate the information it would gain from exploring a candidate location or path before actually visiting it\, and a team of robots can use these predictions to coordinate exploration and manage information sharing under communication constraints. Finally\, we extend reasoning beyond the geometric altogether\, introducing a persistent 3D semantic representation that grounds open-vocabulary vision-language understanding into a structure capable of perceiving objects well beyond the range of onboard depth sensors. This representation enables a single robot to perform long-horizon object search in large outdoor environments\, and allows a team of robots to coordinate search behavior by sharing a sparse\, communication-efficient form of this representation rather than dense maps. \nWe demonstrate this progression through six contributions spanning indoor and outdoor environments and single- and multi-robot deployments\, showing that as representations grow richer–from geometric structure\, to predicted space\, to persistent semantic memory–robots become capable of more efficient exploration\, more effective object search\, and more naturally coordinated teamwork. \n\nThesis Committee:\nSebastian Scherer (Chair)\nYonatan Bisk\nWenshan Wang\nGraeme Best (University of Technology Sydney)\nMicah Corah (Colorado School of Mines)\n\nThesis draft
URL:https://www.ri.cmu.edu/event/spatial-reasoning-and-semantic-representations-for-autonomous-exploration-and-object-search/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260626T100000
DTEND;TZID=America/New_York:20260626T113000
DTSTAMP:20260707T193018
CREATED:20260617T153533Z
LAST-MODIFIED:20260617T170801Z
UID:151617-1782468000-1782473400@www.ri.cmu.edu
SUMMARY:RI PhD Thesis Defense - Angela Chen
DESCRIPTION:Date: June 26\, 2026\nTime: 10:00 – 11:30 AM\nLocation: GHC – Room 4405\nZoom Link\nType: Ph.D. Thesis Defense\nWho: Angela Chen\nTitle: Behavioral Modeling of Interpersonal Dynamics as Controllable Agentic Systems: Empirically-grounded Adaptive Virtual Patients for Psychotherapy Training \nAbstract: The need for mental health care continues to outpace the supply of trained psychotherapists. Training is itself a bottleneck because supervision time is limited and trainees get few chances to practice repeatedly on realistic cases with feedback. Standardized patients played by actors are expensive and hard to scale\, and the hardest clinical moments are not ones a trainee can safely rehearse with real clients. Large language models (LLMs) have made interactive virtual patients practical\, yet most current systems are not empirically grounded: they carry little psychologically meaningful state and focus almost exclusively on one-on-one therapy\, leaving multi-party interaction such as couples therapy unsupported. \nThis thesis treats the design of an LLM-based virtual patient (VP)\, an interactive agent whose behavior must remain coherent over a long interaction\, as a control system. Rather than being steered by a fixed prompt\, the VP’s behavior is governed by a dynamics controller\, so its behavioral trajectory can be inspected and adjusted. The thesis develops this idea in three studies. The first builds LLM-based measures of therapist behavior and client behavior\, applies them to a large corpus of clinical transcripts\, and uses structural equation modeling to estimate how therapist micro-skills affect a client’s disclosure and emotion. The second turns these estimates into an adaptive virtual patient: detectors read the therapist’s empathy and exploration\, a dynamics controller updates the patient’s disclosure state using the estimated coefficients\, and the language model generates a reply at that level of disclosure. The third study extends to a multimodal\, multi-agent interaction system in which two VPs and the therapist interact through a stage controller built around the demand–withdraw pattern common in couples therapy. \nMethodologically\, this thesis shows how to build human–AI interaction systems for a specialized domain that requires explicit behavioral modeling. Concretely\, it delivers a validated pipeline for measuring therapy process\, two training systems for individual and couples therapy\, and a corpus of therapist–agent interactions. Together these broaden access to deliberate practice for trainees\, using AI to strengthen therapists’ preparation rather than substitute for it. \nCommittee:\nHaiyi Zhu (Chair)\, Carnegie Mellon University\nSherry Tongshuang Wu\, Carnegie Mellon University\nAaron Steinfeld\, Carnegie Mellon University\nHolly Swartz\, University of Pittsburgh Medical Center \nLINK TO THESIS DRAFT DOCUMENT
URL:https://www.ri.cmu.edu/event/ri-phd-thesis-defense-angela-chen/
LOCATION:Gates Hillman Center 4405
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260625T133000
DTEND;TZID=America/New_York:20260625T143000
DTSTAMP:20260707T193018
CREATED:20260623T134347Z
LAST-MODIFIED:20260623T134347Z
UID:151646-1782394200-1782397800@www.ri.cmu.edu
SUMMARY:Brain-Aligned Tactile Representations for Dexterous Robot Learning
DESCRIPTION:Abstract: Touch is the essential sensory modality through which animals and robots physically negotiate the world. While much of robotic touch focuses on the capabilities of currently available tactile hardware\, this thesis asks a more general question: what forms of tactile processing and representation could allow robots to approach the dexterity of animals? This thesis argues that simulated force and torque provide a privileged representation of touch: one that is biologically grounded\, computationally scalable\, and effective for dexterous behavior. \n\n\n\n\nFor biological touch\, the rodent vibrissal pathway provides a tractable model system for studying tactile processing.  We train task-optimized temporal networks on realistic whisker force/torque sequences to identify shapes and compare their internal representations against recordings from rats’ barrel cortex. We find that convolutional recurrent models (ConvRNNs) align very closely with neural data\, and contrastive self-supervision with tactile-specific augmentations matches supervised alignment as a label-free proxy.\nIn robotic touch\, we test whether the same class of representation is useful for dexterous behavior. We develop a GPU-parallel tactile sensor simulator that exposes a family of tactile abstractions under one interface\, from binary contact and per-taxel force/torque to marker displacement and temperature\, fast enough to serve as the front-end for dexterous reinforcement learning. For in-hand manipulation tasks\, we find that sensor placement matters more than sensor type or resolution\, that whole-hand coverage closes most of the gap to a privileged teacher. Across 3 dexterous tasks\, per-taxel force/torque emerges as the most useful and robust observation.\nTogether\, these results argue that force and torque are not merely convenient signals to simulate\, but a biologically grounded and behaviorally effective representation of touch. With recurrence and broad spatial coverage\, simulated force and torque provide a common representational substrate for understanding tactile computation in brains and building tactile intelligence in robots.\n\nCommittee:\nProf. Aran Nayebi (Chair)\nProf. Katerina Fragkiadaki\nAkash Sharma
URL:https://www.ri.cmu.edu/event/brain-aligned-tactile-representations-for-dexterous-robot-learning/
LOCATION:Gates Hillman Center 4405
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260625T130000
DTEND;TZID=America/New_York:20260625T140000
DTSTAMP:20260707T193018
CREATED:20260622T141019Z
LAST-MODIFIED:20260622T141019Z
UID:151629-1782392400-1782396000@www.ri.cmu.edu
SUMMARY:Data-Driven Representation and Reasoning for Aviation Safety
DESCRIPTION:Abstract:\nAviation safety analysis increasingly benefits from large-scale operational trajectory data\, yet raw motion traces alone are insufficient for understanding safety-critical events on the airport surface. The significance of an aircraft’s motion depends on the structured operational environment in which it occurs\, including runways\, taxiways\, hold-short lines\, and interactions among multiple agents over time.\n\nThis thesis presents a broader framework for AI-enabled aviation safety and develops several components to support it. Amelia-42 dataset provides a large-scale operational data foundation for studying airport surface movement. Trajectory alignment with airport surface graphs recovers snapped routes\, route-transition predictions\, candidate conflict points\, and time-to-node estimates. To support interpretable safety reasoning\, World2Rules introduces a neural-symbolic pipeline that learns human-interpretable runway-incursion rules from incident reports and nominal operational observations. Critical Scenario Identification mines real runway-incursion reports and evaluates whether models can identify the critical agents and timestamps in safety-relevant interactions. Together\, these components demonstrate how trajectory data can be transformed into structured representations\, interpretable safety rules\, and evaluation methodologies that support the identification\, explanation\, and analysis of safety-critical aviation scenarios.\n\nCommittee:\nDr. Sebastian Scherer (Chair)\nDr. Jean Oh\nJunwon Seo
URL:https://www.ri.cmu.edu/event/data-driven-representation-and-reasoning-for-aviation-safety/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260624T140000
DTEND;TZID=America/New_York:20260624T150000
DTSTAMP:20260707T193018
CREATED:20260622T141205Z
LAST-MODIFIED:20260622T141205Z
UID:151631-1782309600-1782313200@www.ri.cmu.edu
SUMMARY:Towards Socially Intelligent Multi-Agent Systems: Zero-Shot MARL Coordination and Theory-of-Mind Benchmarking of LLM Agents for Strategic Deception
DESCRIPTION:Abstract:\nAn agent that performs well on its own may still struggle when working with others. In multi-agent environments\, success depends not only on understanding the world but also on understanding what other agents know\, intend\, and conceal. Cooperative partners follow hidden conventions\, while adversarial opponents deceive. This work argues that robust multi-agent behavior requires explicit reasoning about these hidden mental states\, and that we must measure this reasoning directly rather than simply looking at task outcomes. \nThese concepts are developed through two complementary projects. The first\, BEACON\, addresses the zero-shot coordination problem: how can an agent coordinate effectively with unfamiliar partners it has never trained with? When agents learn from offline data\, they often lock into dataset-specific conventions that work well with familiar partners but fail with new ones. BEACON is an offline-to-online learning framework that clusters offline trajectories into different conventions\, trains diverse specialists for each convention\, and uses belief-conditioned counterfactual rollouts to adapt online. On 2- and 3-player Hanabi\, BEACON achieves state-of-the-art zero-shot coordination performance while using up to five times fewer training frames than strong online baselines. It also coordinates with human partners comparably to a leading online method. The second project\, AmongUs-X\, asks whether large language model agents genuinely deceive or merely win through other means. Built on the social-deduction game Among Us and spanning 21 model families across more than 8\,700 games\, the benchmark elicits agents’ beliefs at fixed points during meetings. This yields eight Theory-of-Mind metrics measuring detection\, deception\, influence\, and grounding. Win-rate-derived ratings track crewmate detection but miss impostor deception entirely. However\, the elicited beliefs remain well-calibrated\, enabling direct mechanism-level evaluation. \nBoth projects arrive at the same conclusion: high self-play scores can hide poor coordination\, and high win rates can hide absent deception. Modeling other agents’ hidden information and measuring that modeling explicitly is essential for building socially intelligent multi-agent systems and evaluating them reliably. \nCommittee:\nDr. Katia Sycara (chair)\nDr. Jiaoyang Li\nRenos Zabounidis
URL:https://www.ri.cmu.edu/event/towards-socially-intelligent-multi-agent-systems-zero-shot-marl-coordination-and-theory-of-mind-benchmarking-of-llm-agents-for-strategic-deception/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260623T120000
DTEND;TZID=America/New_York:20260623T133000
DTSTAMP:20260707T193018
CREATED:20260610T141042Z
LAST-MODIFIED:20260610T141042Z
UID:151561-1782216000-1782221400@www.ri.cmu.edu
SUMMARY:From Margin to Center: Designing Inclusive & Equitable Service Robots with Disabled Adults
DESCRIPTION:Abstract\n\n\nService robots – autonomous systems that perform personal and professional tasks – have become a common sight in the Global North. In Human-robot interaction (HRI)\, researchers rarely consider the design implications of service robots for people with disabilities (PwDs) beyond controlled assistive contexts\, such as private homes and assisted living facilities. Nevertheless\, the purview of PwDs transcends assistive contexts: PwDs know from lived experience that autonomous innovations can cause real harm to members of the disabled community when deployed. Therefore\, considering the lived experiences of PwDs\, many of whom exist at the social margins of society\, can enable the design of inclusive service robots. Even so\, robotics design practice can be exclusionary for people who are neither roboticists nor robot design practitioners.\n\nParticipatory Design (PD)\, or cooperative design (“co-design”)\, an equitable approach aimed at shifting power relations and integrating community interests in design processes outcomes\, is becoming commonplace in HRI. However\, HRI design practitioners who work with PwDs seldom interrogate whose lived experiences are represented in the design process – commonly privileging the experiences of professionals\, such as technical experts and medical practitioners; this further encodes normative biases about how PwDs may want robots to behave around them or the types of support they may desire. Instead\, HRI design practitioners should center on the experience-as-knowledge of members of the disabled community within co-design processes.\n\nIn this thesis\, I present the lived experiences as a PD methodological framework for designing service robots with disabled adults. I demonstrate the application of this framework by co-designing service robots with disabled adults across three domains: assistive navigation\, last-mile delivery\, and mental well-being. I highlight the principles of the lived experience framework\, which reflects an onto-epistemological commitment to re-position the experience-as-expertise of PwDs\, including shifting power towards PwDs through accessible design communication; highlighting power relations\, embracing tensions\, and fostering meaningful engagements with PwDs with intersecting social marginalization; and encouraging reflexivity in the co-design process. Furthermore\, I offer design guidelines and socio-technical considerations for developers and design practitioners who wish to create inclusive service robots and equitable human-robot experiences. This work is a purposeful demonstration of the meaningful contributions made by democratizing the design of enabling\, yet disruptive\, autonomous systems and a call-to-action for HRI practitioners who seek renewed commitments to PD as a transformative framework.\n\n\nThesis Committee Members:\nAaron Steinfeld\, Chair\nJean Oh\, RI\nPatrick Carrington\, HCII\nCynthia Bennett\, Google\n\nURL link to thesis
URL:https://www.ri.cmu.edu/event/from-margin-to-center-designing-inclusive-equitable-service-robots-with-disabled-adults/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260622T110000
DTEND;TZID=America/New_York:20260622T120000
DTSTAMP:20260707T193018
CREATED:20260616T135241Z
LAST-MODIFIED:20260616T135241Z
UID:151605-1782126000-1782129600@www.ri.cmu.edu
SUMMARY:Towards Generalizable Embodied Navigation with Vision-Language Models
DESCRIPTION:Abstract:\nEmbodied navigation asks an autonomous agent to move through unknown environments and accomplish tasks such as finding objects or following instructions. Reliable performance in real-world settings\, from household assistance to warehouse logistics\, requires the agent to tightly integrate perception\, semantic reasoning\, and long-horizon planning under cluttered layouts\, ambiguous appearances\, and robot-specific constraints. Vision-language models (VLMs) offer rich semantic priors for this task\, but directly inserting them into the navigation loop often leads to inefficient exploration\, unstable behavior\, and limited transfer across platforms. This thesis argues that these failures stem from a multi-level misalignment between how VLMs reason and what navigation demands\, and presents four complementary contributions to address it. STRIVE shows that object navigation improves substantially when the environment is summarized as a structured graph of objects\, viewpoints\, and rooms\, letting the VLM reason at a semantic level while classical algorithms handle local exploration. SysNav extends this into a deployable system by decoupling semantic reasoning\, room-level planning\, and embodiment-specific control for robust cross-platform deployment. IntentNav shifts from prompting to learning\, showing that navigation decisions become more stable when trained with intent-aligned supervision from human demonstrations. Recognizing that object-goal search underutilizes VLM reasoning\, Goal2Pixel moves to instruction-guided navigation where longer\, compositional instructions demand richer language grounding\, and reformulates the task as pixel grounding so the model directly connects instruction understanding to executable motion. Together\, these works trace a progression from structured representation through system integration and learned decision making to instruction-guided navigation\, arguing that effective embodied navigation with VLMs requires aligning reasoning with the right representations\, architectures\, learning objectives\, and task formulations.\n \nCommittee:\nProf. Ji Zhang (Chair)\nProf. Wenshan Wang\nZhixuan Liu
URL:https://www.ri.cmu.edu/event/towards-generalizable-embodied-navigation-with-vision-language-models/
LOCATION:Newell-Simon Hall 3305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260618T110000
DTEND;TZID=America/New_York:20260618T123000
DTSTAMP:20260707T193018
CREATED:20260608T191139Z
LAST-MODIFIED:20260608T191612Z
UID:151508-1781780400-1781785800@www.ri.cmu.edu
SUMMARY:Integrating Learning and Collaboration for Human-Robot Alignment
DESCRIPTION:Abstract: The alignment problem for robots considers how robots can learn to behave in accordance with human values. Today\, robot learning paradigms enable humans to provide data (e.g.\, preference labels or demonstrations)\, which the robot uses to update its behavior (e.g.\, reward model or policy) to better align with human intentions.  However\, the current paradigm requires the human to constantly supervise\, provide new feedback\, and—more fundamentally—perfectly understand where the robot is misaligned. Even if the robot eventually learns a perfect model of how the human wanted it to behave\, the overall human-robot interaction during alignment could have been demanding\, confusing\, or arduous for the person.\n\nThis dissertation argues that alignment should not be viewed solely as a problem of learning the correct task behavior\, but also as a problem of supporting humans throughout the alignment process itself. We propose that the learning process should be fundamentally collaborative\, in which robots actively participate in alignment through introspection\, communication\, uncertainty estimation\, sharing control\, and interaction planning.Our early works investigate how robots can learn and adapt collaborative strategies from interaction\, including adapting to partner preferences and proactively communicating robot capabilities to improve coordination. We then introduce model-agnostic uncertainty quantification methods for interactive robot learning using online conformal prediction\, enabling robots to calibrate deployment-time uncertainty from intermittent human feedback. Building on these capabilities\, we develop collaborative active learning methods in which robots request assistance when uncertain\, calibrate intervention policies using human feedback\, strategically allocate human effort across multitask learning settings\, and negotiate control handoffs through shared-control interaction mechanisms. Finally\, we study the relationship between uncertainty and human intervention behavior\, showing that robot uncertainty and human judgments of intervention necessity are only partially aligned\, motivating methods that explicitly calibrate robot assistance requests to human preferences. The outcomes are robots that know when they do not know\, communicate uncertainty\, request help strategically\, support human teachers during learning\, and adapt interaction itself as part of the alignment process.Thesis Committee Members: \n\nHenny Admoni (Chair)\nReid Simmons (Chair)\nAndrea Bajcsy\nAnirudha Majumdar (Princeton)\n\n\n\nURL link to thesis
URL:https://www.ri.cmu.edu/event/integrating-learning-and-collaboration-for-human-robot-alignment/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260618T100000
DTEND;TZID=America/New_York:20260618T110000
DTSTAMP:20260707T193018
CREATED:20260615T135538Z
LAST-MODIFIED:20260615T135538Z
UID:151598-1781776800-1781780400@www.ri.cmu.edu
SUMMARY:Embodied Design\, Modeling\, and Optimization for Bio-Inspired Aquatic Robots
DESCRIPTION:Abstract:\nBio-inspired aquatic robots offer a promising route to agile and efficient locomotion in fluid environments\, where conventional rigid systems remain limited. In bio-inspired aquatic systems\, locomotion is not determined by actuation or control alone\, but instead emerges from tightly coupled interactions among body morphology\, distributed compliance\, actuation\, onboard sensing\, and the surrounding flow\, making analysis\, design\, and optimization fundamentally challenging. \nThis thesis studies these coupled problems through the development of embodied aquatic robot platforms\, variational dynamical models\, and integrated sensing strategies\, with particular emphasis on undulatory and flapping swimming\, variable-stiffness design\, strongly coupled fluid–robot interaction\, and flow sensing and feedback based on local pressure measurements. The goal is to understand how morphology\, dynamics\, and sensing can be jointly exploited within a continuous-time dynamical framework to realize robotic swimmers that are more efficient\, maneuverable\, and adaptive\, while also enabling reliable optimization of physical design\, sensing configuration\, and control through optimization-based fluid dynamic formulations and high-fidelity gradients. These ideas are further examined and validated through experiments on physical robotic platforms. \nCommittee:\nProf. Carmel Majidi\nProf. Zachary Manchester\nProf. Victoria Webster-Wood\nDr. Jeong Hun Lee
URL:https://www.ri.cmu.edu/event/embodied-design-modeling-and-optimization-for-bio-inspired-aquatic-robots/
LOCATION:Gates Hillman Center 4405
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260618T100000
DTEND;TZID=America/New_York:20260618T110000
DTSTAMP:20260707T193018
CREATED:20260615T135404Z
LAST-MODIFIED:20260615T135404Z
UID:151596-1781776800-1781780400@www.ri.cmu.edu
SUMMARY:Towards Fine-Grained Diagnosis of GUI Agents
DESCRIPTION:Abstract: \nGraphical User Interface (GUI) agents need strong planning—what to do next—and grounding—where to click next—to solve user tasks. Yet these agents remain unreliable\, and standard metrics such as task success or next-action accuracy often obscure why they fail. In this talk\, I argue that reliable GUI agents require fine-grained diagnosis of core agentic capabilities\, studied through two complementary directions. \nFirst\, I examine planning failures in GUI agents. Existing work can often identify planning errors\, but rarely pinpoints which component failed. This is largely because there is no structured breakdown of the skills an agent must master to plan effectively. This thesis identifies several such skills and proposes synthetic data pipelines to diagnose failures across them. Surprisingly\, even the strongest agents often fail on basic skills\, such as temporal reasoning over webpage sequences. I further show that performance on these fine-grained planning skills correlates with downstream task success\, suggesting a cheaper way to predict agent performance without expensive evaluations. \nSecond\, I diagnose failures in GUI grounding. Traditional accuracy-based metrics do not reliably expose hidden failure modes\, and I argue that sensitivity-based metrics are needed to compare models. In particular\, I find that no model consistently clicks the same UI element\, such as a calendar button\, across diverse user scenarios\, apps\, and operating systems. This raises concerns about whether these models truly understand what they click. To operationalize this diagnosis\, I propose a novel data generation pipeline and a GUI grounding diagnosis agent that automatically generates diverse instructions\, uses model feedback\, and iteratively identifies failure cases across state-of-the-art grounding models. Results show a significant mismatch between model rankings under accuracy-based metrics and our sensitivity-based metrics\, highlighting the need for more reliable agent performance measures. \nTogether\, this thesis provides diagnostics that can guide model development and advance more robust\, trustworthy interactive AI systems. Our future work delineates how each of these diagnostics can serve as a simple intervention to improve existing GUI agent pipelines. \nCommittee: \nFernando De La Torre\, co-chair\nAndrea Bajcsy\, co-chair\nLouis-Philippe Morency\nYinong (Oliver) Wang
URL:https://www.ri.cmu.edu/event/towards-fine-grained-diagnosis-of-gui-agents/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260616T140000
DTEND;TZID=America/New_York:20260616T153000
DTSTAMP:20260707T193018
CREATED:20260610T142430Z
LAST-MODIFIED:20260610T142621Z
UID:151566-1781618400-1781623800@www.ri.cmu.edu
SUMMARY:Scalable Oversight Across Generative Visual AI: Toward Visual Storytelling for Everyone
DESCRIPTION:Abstract: \nGenerative visual AI has advanced by scaling data and compute\, but its next bottleneck is oversight: the expert signals that evaluate\, reward\, and teach models what “good” looks like. Providing such oversight is increasingly difficult because foundation vision-language models now match or surpass most humans at the skills being judged. \nThis thesis develops scalable oversight for generative visual AI\, turning limited expert judgment into abundant and reliable evaluation\, reward\, and training signals. First\, I present VQAScore and GenAI-Bench\, evaluation standards for text-to-visual generation adopted by 100+ frontier labs such as DeepMind\, and show that a stronger judge directly improves generation through inference-time scaling. Second\, I extend oversight to video\, where precise language must be built rather than collected: CameraBench (NeurIPS’25 Spotlight) defines a structured cinematic vocabulary with professional creators\, and CHAI (CVPR’26 Highlight) introduces critique-based human-AI oversight in which experts critique model drafts rather than write from scratch. One recipe of specification\, oversight\, and post-training simultaneously improves captioning\, reward modeling\, and video generation\, enabling a small open model to surpass proprietary models like GPT and Gemini. Finally\, I present Moodio\, a deployed AI film studio with hundreds of daily active users\, whose cinematic video retrieval outperforms SOTA embedding models and measurably helps creators generate more compelling videos. I conclude with ongoing work on personalized oversight for end-to-end creative agents\, toward visual storytelling for everyone. \nThesis Committee Members: \n\nDeva Ramanan (Chair)\nDeepak Pathak\nGraham Neubig\nAli Farhadi (University of Washington and Microsoft)
URL:https://www.ri.cmu.edu/event/scalable-oversight-across-generative-visual-ai-toward-visual-storytelling-for-everyone/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260616T100000
DTEND;TZID=America/New_York:20260616T113000
DTSTAMP:20260707T193018
CREATED:20260609T175151Z
LAST-MODIFIED:20260609T175151Z
UID:151545-1781604000-1781609400@www.ri.cmu.edu
SUMMARY:Aligning Observations Across Viewpoint\, Time\, and Embodiment for Agricultural Perception and Manipulation
DESCRIPTION:Abstract:\n\nAgricultural specialists are actively turning to robotic and computer vision-based systems to reduce the manual labor required to inspect and manipulate crops. These tasks require robots to perceive and interact with plants from partial\, localized observations\, often in dense and cluttered environments. For perception\, a central challenge is that crops are small\, are easily occluded\, and may change in appearance and position over time. For manipulation\, the ability to learn visuomotor policies is limited by the lack of available datasets and the difficulty of collecting robot demonstrations in the field. This thesis addresses these challenges by aligning and associating partial observations across viewpoint and time for agricultural perception\, and across viewpoint and embodiment for learning wrist-camera manipulation policies from human demonstrations. \nIn the first part of this thesis\, we develop perception-based methods for visually inspecting small crops in agriculture from limited observations. We present a 3D reconstruction pipeline for non-destructive seed counting of sorghum panicles\, a next-best-view planning approach for autonomously imaging and sizing apple fruitlets\, and a transformer-based method for spatio-temporally associating apple fruitlets across days and viewpoints. \nThe second part of this thesis shifts towards robot manipulation and learning from human demonstrations. We present a method that transforms monocular egocentric human demonstrations into wrist-camera observations and robot actions for training visuomotor policies\, without requiring depth sensors\, multi-view camera setups\, or custom data collection hardware. Building on this work\, we propose to align egocentric and wrist-camera observations and actions in latent space\, reducing reliance on explicit object tracking and image-space rendering. We further propose to incorporate visuo-tactile sensing for grape cluster inspection and harvesting. Together\, these efforts investigate how aligning observations can support agricultural robots that reason from limited visual information and learn manipulation policies when robot data is difficult to collect. \n\nThesis Committee Members:\nGeorge Kantor (Chair)\nDavid Held\nJeffrey Ichnowski\nSoumik Sarkar (Iowa State University)\n \nThesis Proposal Draft
URL:https://www.ri.cmu.edu/event/aligning-observations-across-viewpoint-time-and-embodiment-for-agricultural-perception-and-manipulation/
LOCATION:1305 Newell Simon Hall
CATEGORIES:PhD Thesis Proposal,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260615T130000
DTEND;TZID=America/New_York:20260615T143000
DTSTAMP:20260707T193018
CREATED:20260608T193629Z
LAST-MODIFIED:20260608T193629Z
UID:151519-1781528400-1781533800@www.ri.cmu.edu
SUMMARY:Physical Process-Informed Mapping for Robotic Exploration
DESCRIPTION:Abstract:\nMobile robots used for information gathering tasks rely on dense\, predictive mapping of large-scale regions to determine where to take measurements. Current approaches to mapping commonly rely on Gaussian process regression to spatially correlate data\, extrapolate from sparse samples\, and estimate uncertainty. However\, these approaches do not incorporate meaningful information about physical processes that contribute to the scientific observations being recorded\, instead focusing on only predicting measurements or relying on black box expert-based systems to incorporate physically meaningful information.\n\nIn this work\, we introduce physical process-informed mapping\, a framework for integrating scientific knowledge about physical processes into mapping for mobile exploration robots. This approach provides two advantages for exploration. First\, the hierarchical statistical model underlying the mapping framework is capable of providing predictions and uncertainty estimates for both the sensor measurements and the latent natural phenomena driving these measurements in a dense\, probabilistic manner. Second\, various components of the mapping approach provide methods to measure and adjust the importance of models of scientific knowledge\, enabling learning about the conditions under which these models are more or less relevant to the latent phenomenon. Taken together\, these methods improve the interpretability of mapping for autonomous scientific discovery\, enabling future robotic explorers to take advantage of a more in-depth representation of information\, and can provide increases in the predictive accuracy of mapping.\n\nThesis Committee Members:\nDavid Wettergreen (Chair)\nWennie Tabib\nMikael Kuusela\nTerrence Fong (NASA Ames Research Center)\n\n\nThesis Draft
URL:https://www.ri.cmu.edu/event/physical-process-informed-mapping-for-robotic-exploration-2/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260615T110000
DTEND;TZID=America/New_York:20260615T123000
DTSTAMP:20260707T193018
CREATED:20260604T191624Z
LAST-MODIFIED:20260604T191624Z
UID:151486-1781521200-1781526600@www.ri.cmu.edu
SUMMARY:Integrating Structured Knowledge for State and Geometry Estimation
DESCRIPTION:Abstract:\n\nReliable state and geometry estimation from limited observations is a fundamental challenge in robotics and perception. Observations are often noisy\, partial\, or ambiguous\, making estimation ill-posed without additional structure. This thesis argues that robust estimation in these regimes is enabled by integrating structured knowledge into the inference process. \nEstimation can be viewed as inferring latent state or geometry by combining three complementary forms of structure: constraints that restrict the feasible solution space\, physical models that describe how observations are generated\, and priors that regularize underdetermined solutions. \nFirst\, constraints provide structure in the solution space. We develop methods for imposing hard constraints in real-time estimation through incremental optimization\, and for learning well-conditioned measurement noise models that shape the optimization landscape. We also study constraints learned from demonstrations and analyze when they can serve as general state constraints. \nSecond\, physical models provide structure in the observation process. We study settings where sensing physics fundamentally limits observability\, and show how incorporating physics-based forward models into neural rendering enables accurate 3D reconstruction and resolves ambiguities in acoustic and multimodal sensing. \nFinally\, priors provide structure when estimation remains underdetermined even after modeling constraints and physics. We demonstrate how priors learned from data-rich visual domains can be transferred to data-scarce sensing modalities\, enabling pose-free acoustic 3D reconstruction and tactile 3D reconstruction from sparse robot touches. \nTogether\, these contributions provide a unified perspective on estimation as the integration of constraints\, physical models\, and priors. \nThesis Committee Members:\n\nMichael Kaess (Chair)  \nIoannis Gkioulekas\nShubham Tulsiani\nNikolay Atanasov (University of California – San Diego)\n\n \nURL link to thesis: Thesis Document
URL:https://www.ri.cmu.edu/event/integrating-structured-knowledge-for-state-and-geometry-estimation/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260611T123000
DTEND;TZID=America/New_York:20260611T133000
DTSTAMP:20260707T193018
CREATED:20260604T185218Z
LAST-MODIFIED:20260604T185218Z
UID:151483-1781181000-1781184600@www.ri.cmu.edu
SUMMARY:Red and Blue Teaming for Robust Manipulation under Geometric Variations
DESCRIPTION:Abstract:\nRobotic manipulation policies are typically evaluated on curated\, in-distribution test sets\, which offer limited insight into how these policies behave under plausible variation. One important source of this variation is geometric in nature\, arising from small changes in object geometry that quietly alter grasp affordances and contact dynamics. Rather than treating robustness as a property to be measured passively\, this thesis develops methods that use object geometry as a controllable axis along which a policy can be actively stress-tested and subsequently improved.\n\nWe first introduce Geometric Red-Teaming (GRT)\, a framework that automatically discovers CrashShapes: structurally valid\, user-constrained mesh deformations that induce catastrophic failures in pre-trained manipulation policies. GRT couples a Jacobian-field deformation model with a gradient-free\, simulator-in-the-loop optimization strategy\, allowing it to search over plausible object geometries while treating the policy being tested as a black box. Across insertion\, articulation\, and grasping tasks\, GRT consistently finds deformations that collapse policy performance\, exposing brittle failure modes that static benchmarks miss. We further show that fine-tuning on a small set of CrashShapes together with the nominal object\, a process we call blue-teaming\, improves task success by up to 60 percentage points on those shapes while preserving performance on the original object. We validate both red-teaming and blue-teaming on a real robot arm\, where simulated CrashShapes reduce task success from 90% to as low as 22.5%\, and blue-teaming recovers performance to as high as 90% on the corresponding real-world geometry.\n\nWhile GRT provides a direct evaluation tool for geometric robustness\, its simulator-in-the-loop search takes hours per object and therefore cannot supply CrashShapes for large-scale blue-teaming. To address this limitation\, we introduce CrashGen\, a point-cloud-conditioned diffusion model that amortizes this search by generating failure-inducing deformation controls in seconds rather than hours. A deterministic Jacobian-field operator then maps these controls to physically plausible deformations of the original mesh. Trained once on controls discovered by running GRT offline\, CrashGen generates failure-inducing variants for over 80% of held-out objects under simulation validation. Such variants can in turn serve as targeted training data: blue-teaming Contact-GraspNet on a mixture of original meshes and CrashGen variants improves grasp success on held-out objects by up to 21% in simulation and 30% on a real robot\, relative to a baseline fine-tuned only on the original meshes.\n\nTogether\, GRT and CrashGen show that object geometry can serve two complementary roles in making manipulation policies more reliable under geometric variation. GRT provides an active evaluation tool for identifying policy-critical geometric perturbations\, while CrashGen provides a constructive mechanism for turning those perturbations into scalable training data. Once an expensive diagnostic\, geometric failure discovery becomes a scalable source of targeted supervision for the very policies it stress-tests.\n\nCommittee:\nZackory Erickson (co-chair)\nDavid Held (co-chair)\nAndrea Bajcsy\nYufei Wang
URL:https://www.ri.cmu.edu/event/red-and-blue-teaming-for-robust-manipulation-under-geometric-variations/
LOCATION:NSH 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260611T100000
DTEND;TZID=America/New_York:20260611T113000
DTSTAMP:20260707T193018
CREATED:20260528T204739Z
LAST-MODIFIED:20260529T163943Z
UID:151395-1781172000-1781177400@www.ri.cmu.edu
SUMMARY:Autonomous Crop Manipulation: From Model-Based Reasoning to Learned Interaction
DESCRIPTION:Abstract: Robots that manipulate crops must contend with plants that occlude themselves\, deform under contact\, and make the manipulator contact structures it neither targets nor sees in advance. This thesis argues that autonomous manipulation of crops in unstructured agricultural environments is best advanced not by choosing between model-based and learned approaches\, but by integrating them where each is strongest: model-based components provide explicit geometric priors and safety constraints for contact-rich interaction\, while learned components capture plant dynamics and visuomotor behaviors that resist hand-engineering. \nFour technical contributions develop this argument. The first is a model-based skeletonization method that recovers tree branch structure from RGB-D views using an occlusion-likelihood field and a min-cost path search to reason explicitly about unobserved geometry. The second is a graph neural network trained on mass-spring-damper simulations that predicts how a tree deforms under push. The third is an end-to-end diffusion policy trained on demonstrations collected with a handheld shear-gripper that drives pepper harvesting in an unprotected outdoor field. The fourth is a reactive safety layer that wraps a learned visuomotor policy with a control barrier function quadratic program whose constraints are drawn from an occupancy map updated online from contact estimates\, enabling safe reaching through cluttered canopies. \nTaken together\, the four contributions trace a path from offline geometric reasoning about static plants to safe contact-rich interaction in clutter\, showing that robust agricultural manipulation emerges not from end-to-end learning alone\, but from a synthesis in which model-based safety enables reliable learned interaction. \nCommittee:\nGeorge Kantor (chair)\nOliver Kroemer\nDave Held\nMaren Bennewitz (University of Bonn) \nThesis Draft
URL:https://www.ri.cmu.edu/event/autonomous-crop-manipulation-from-model-based-reasoning-to-learned-interaction/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260610T100000
DTEND;TZID=America/New_York:20260610T113000
DTSTAMP:20260707T193018
CREATED:20260601T184629Z
LAST-MODIFIED:20260601T184629Z
UID:151406-1781085600-1781091000@www.ri.cmu.edu
SUMMARY:Annotation-Free Learning for Mobile Robot Navigation in Unstructured Environments
DESCRIPTION:Abstract: Navigation in unstructured environments is a capability critical to many robotics applications such as forestry\, construction\, disaster response and defense. In these domains\, robots have the potential to eliminate much of the dull\, dirty and/or dangerous work that is currently performed by humans. Unfortunately\, these environments pose a unique set of challenges for navigation not present in controlled settings such as warehouses and highway driving.\n\nWe argue that the key challenge in deploying robots in unstructured environments is the difficulty of interpreting high-dimensional streams of multi-modal perception data\, and translating them into robust\, expressive representations for downstream path planning and control. This problem is exacerbated by the fact that unstructured environments are highly uncertain and lack a clean mapping from semantics to navigation decisions (i.e. traversability). \nWhile there have been notable successes in deploying robots in unstructured environments\, such systems are highly tuned to a given robot platform and environment\, and are the result of months to years of dedicated effort from a highly skilled team of engineers. As such\, deployment of these systems to new platforms or environments requires extensive tuning of perception-planning interfaces\, labeling of semantic images\, etc. Unfortunately\, this makes engineering expertise the bottleneck in the widespread adoption of mobile robots. \nI will present my work in designing a learning-based perception and traversability system for high-speed off-road driving from camera and LiDAR data. Importantly\, this approach is annotation-free\, enabling performance matching or exceeding current SoTA in off-road driving from a relatively small amount of tele-operated demonstrations. \n\nCommittee:\nSebastian Scherer\, chair\nAaron Johnson\nDrew Bagnell\nWenshan Wang\nDavid D. Fan (Field AI)\n \n\nThesis Draft
URL:https://www.ri.cmu.edu/event/annotation-free-learning-for-mobile-robot-navigation-in-unstructured-environments/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260609T140000
DTEND;TZID=America/New_York:20260609T153000
DTSTAMP:20260707T193018
CREATED:20260601T172413Z
LAST-MODIFIED:20260601T172413Z
UID:151404-1781013600-1781019000@www.ri.cmu.edu
SUMMARY:Scaling Sim2Real Learning for Robot Manipulation
DESCRIPTION:Abstract: \nRecent progress in robot learning has led to impressively capable manipulation systems. Much of the progress has come from scaling up human demonstrations; however\, collecting such data through manual teleoperation is slow\, costly\, and hard to scale.\n\nPhysics-based Simulation offers a scalable\, safe\, and efficient alternative for generating large demonstration datasets. However\, some core challenges limit the full potential of this approach: the heavy manual effort required to design simulation tasks and rewards\, the gap between simulation and reality\, and the difficulty of learning policies that generalize well when trained on large\, diverse simulation datasets.\n\nIn this thesis\, we tackle these challenges through scalable\, generalizable\, and adaptive robot learning. First\, I will show how structured policy representations can enable simulation trained policies to achieve broad generalization in the real world and serve as a strong prior for downstream fine-tuning. Second\, I will introduce the Generative Simulation pipeline for automatic generation of large-scale simulation datasets with minimal human efforts. Finally\, I will discuss some novel algorithms for efficient adaptation of simulation-trained policies to the real world. Together\, these efforts move us toward robots that can learn broadly\, adapt quickly\, and assist people in real homes and workplaces.\n\nCommittee:\nZackory Erickson (co-chair)\nDavid Held (co-chair)\nKaterina Fragkiadaki\nChuang Gan (UMass Amherst and MIT-IBM Watson AI Lab)\nDieter Fox (University of Washington and Ai2)\nLink to draft thesis
URL:https://www.ri.cmu.edu/event/scaling-sim2real-learning-for-robot-manipulation/
LOCATION:1305 Newell Simon Hall
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260608T123000
DTEND;TZID=America/New_York:20260608T140000
DTSTAMP:20260707T193018
CREATED:20260602T163009Z
LAST-MODIFIED:20260602T163009Z
UID:151481-1780921800-1780927200@www.ri.cmu.edu
SUMMARY:Design and Evaluation of Low-Cost\, Open-Source Haptic Interfaces for Diverse Learning Applications
DESCRIPTION:Abstract: Touch is a powerful yet underused channel for learning. Prior research shows that haptic interaction can support both sensorimotor skill acquisition and the understanding of abstract concepts by grounding learning in bodily experience. However\, most haptic devices remain expensive\, technically complex\, and difficult to reproduce\, which keeps them largely confined to specialized laboratories. This limits their use in education and rehabilitation and has slowed progress toward scalable\, low-cost\, open-source solutions\, as well as toward a systematic understanding of how affordable haptic devices should be designed to reliably produce learning benefits. As a result\, the broader learning potential of haptics remains underexplored\, especially across diverse domains and beyond measures of immediate task success.\nThis thesis examines the design and evaluation of haptic systems for learning across three distinct domains. The first system\, HaptiClay\, explores how haptics and gesture can support mathematics learning by helping students construct concrete representations of terms in polynomial functions. The thesis traces the iterative design of the device and reports interventions with students that use haptics to encourage gestural movements while molding polynomial functions and relate those gestures to specific terms in the polynomials. We then analyze learning outcomes to understand the effectiveness of the intervention. The second system\, DexKit\, enables students to experience dexterity concepts in dexterous teleoperation through touch\, including robotic manipulation control\, object interaction\, and stiffness variation. It introduces a dexterous manipulation platform that combines a soft robotic hand with a three-finger haptic interface\, including a novel two-degree-of-freedom mechanism for the index and middle fingers and a soft delta mechanism for the thumb. The third system\, VibroGait\, is a wearable haptic device for gait correction that helps users learn improved walking patterns through vibrotactile feedback. The thesis presents the design of a flexible skin-interfacing device\, the gait prediction algorithms and their implementation\, and studies comparing multiple haptic feedback patterns for gait correction. \nAcross these case studies\, the thesis investigates how effective\, low-cost learning tools can be designed\, which design principles generalize across domains\, how haptics influence learning beyond task success\, and how haptic systems for learning can be evaluated rigorously. By bringing together mathematics learning\, robotic teleoperation\, and gait correction\, this work expands the evidence base for accessible haptic learning technologies and contributes practical design knowledge for future low-cost\, open-source haptic systems. \n\nCommittee\nMelisa Orta Martinez (chair)\nJames McCann\nEni Halilaj\nKylie Peppler (University of California\, Irvine)\n\n\nThesis Proposal Draft
URL:https://www.ri.cmu.edu/event/design-and-evaluation-of-low-cost-open-source-haptic-interfaces-for-diverse-learning-applications/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:PhD Thesis Proposal,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260604T110000
DTEND;TZID=America/New_York:20260604T123000
DTSTAMP:20260707T193018
CREATED:20260529T154504Z
LAST-MODIFIED:20260529T154504Z
UID:151398-1780570800-1780576200@www.ri.cmu.edu
SUMMARY:User Intent-Driven and Context-Aware Personalization for Assistive Exoskeletons
DESCRIPTION:Abstract:\nPersonalizing exoskeleton control to individual preferences is crucial for real world deployment. Data-driven approaches have enabled user-generalizable controllers\, yet conventional personalization methods optimize biomechanical cost functions over user preferences. Prior work shows that users can perceive and report their preferred parameters\, yet no lightweight method maps user intent to quantitative control parameter changes in real time. \nTo address this gap\, we present a vision-language model (VLM) guided human-exoskeleton interface that translates natural user feedback and egocentric visual context into parameter updates for a hip exoskeleton controller. Our framework uses an off-the-shelf VLM that performs high-level intent parsing\, while low-level action selection is offloaded to a contextual bandit. This bandit explores and exploits over two personalization parameters\, the magnitude of assistance and the offset timing\, while learning individual preferences over time. We use human feedback (Likert scale) ratings as rewards for the bandit\, iterating parameter updates until the user reports satisfaction (Likert ≥ 5). \nWe evaluated this framework across a five-task locomotion track\, spanning level ground\, ramps\, and stairs\, across two laps\, against a single-shot interpreter and a one-shot-feedback baseline. Our contextual bandit pipeline improved from default initializations within a single interaction\, raising satisfaction by an average of 3 Likert points in multi-rating loops. No-improvement personalization instances were eliminated by the second lap\, and user satisfaction interactions increased from 67% to 95% across laps. We demonstrate VLMs paired with contextual bandits as an effective framework for online\, context-aware\, user preference learning in real-time exoskeleton personalization. \n\nCommittee:\nDr. Inseung Kang (chair)\nDr. Hartmut Geyer\nMichelle Zhao
URL:https://www.ri.cmu.edu/event/user-intent-driven-and-context-aware-personalization-for-assistive-exoskeletons/
LOCATION:Newell-Simon Hall 3305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260601T143000
DTEND;TZID=America/New_York:20260601T153000
DTSTAMP:20260707T193018
CREATED:20260526T132047Z
LAST-MODIFIED:20260526T132047Z
UID:151385-1780324200-1780327800@www.ri.cmu.edu
SUMMARY:Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
DESCRIPTION:Abstract:\nImitation learning has emerged as a leading paradigm for teaching manipulation skills to robots\, but its success depends on the costly endeavour of collecting robot demonstrations through teleoperation. Generalizing to novel objects\, environments\, and task variations typically requires massive datasets that are expensive to scale. This thesis investigates an alternative lever: hierarchy—explicitly factorizing manipulation policies into high-level reasoning about where to go and low-level control about how to get there—and asks whether such a factorization can both improve data efficiency from robot demonstrations alone and enable the use of\ncheaper sources of data such as human video.\n\nWe present GHOST\, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy that predicts the next sub-goal as a distribution over 3D end-effector poses from multi-view RGB-D observations\, and (ii) a low-level goal-conditioned controller that executes embodiment-specific actions. To condition image-based policies on 3D goals\, we introduce a simple spatial interface that projects predicted goals into the image plane and represents them as end-effector heatmaps. Across a suite of manipulation tasks\, this hierarchical factorization consistently improves performance and robustness compared to a flat Diffusion Policy. We further show that this hierarchical interface makes it easy to incorporate human demonstrations without relying on (noisy) action retargeting. Because sub-goals are largely embodiment-agnostic\, we train\nthe high-level policy on human video to specify how learned skills should be applied and composed\, while keeping the low-level policy trained purely on robot data. This hierarchy enables adaptation to novel objects and task variations using only a small number of human demonstrations.\n\nWe evaluate GHOST on a suite of manipulation tasks spanning pick-and-place\, cloth folding\, and tool-use\, demonstrating both improved in-distribution performance and meaningful cross-embodiment generalization from human video. We conduct ablations to further isolate the contributions of the hierarchical factorization\, and identify the visual domain gap between human and robot observations as a significant bottleneck for long-horizon generalization. \nCommittee:\nDavid Held (advisor)\nShubham Tulsiani\nAviral Kumar\nYilin Wu
URL:https://www.ri.cmu.edu/event/hierarchical-sub-goal-policies-for-generalizing-robot-manipulation/
LOCATION:Gates Hillman Center 4405
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260526T133000
DTEND;TZID=America/New_York:20260526T143000
DTSTAMP:20260707T193018
CREATED:20260520T151822Z
LAST-MODIFIED:20260520T151822Z
UID:151376-1779802200-1779805800@www.ri.cmu.edu
SUMMARY:Explore and Exploit: Learning Policies for Efficient and Coordinated Active Search
DESCRIPTION:Abstract:  \nRobotic search is becoming a central capability in domains where the world is large\, uncertain\, and costly to inspect directly: search and rescue\, environmental monitoring\, surveillance\, and infrastructure inspection. In these settings\, the hard problem is not perception alone but the online sensing decision: where to look next as evidence arrives\, while every motion command spends travel distance and mission time. Active search requires both exploration and exploitation: reducing uncertainty across the searchable region\, and using the emerging posterior to confirm likely objects of interest. This thesis studies that decision problem under a shared Bayesian active-search formulation\, and develops learned policies for real-time field execution and coordination across heterogeneous teams. \nThe first contribution is a path-aware single-robot policy. We introduce a path-integral expert that scores complete shortest-path routes by their accumulated expected information gain\, then amortize that expensive expert into a Graph Attention Network policy by behavior cloning. The resulting policy achieves up to a 254x speedup over the path-integral expert and an 11x speedup over a one-step greedy expert\, generalizes across map geometries and object densities without retraining\, and yields up to 2.86x faster mission completion against a strong NATS baseline in field tests on an autonomous ground vehicle in a 75\,000 square meter forested environment. \nThe second contribution extends the same shared-belief interface from single-robot search to coordinated teams. We propose QUEST\, a multi-agent Q-learning framework that trains over the same graph backbone but optimizes a per-decision Bellman target over downstream belief\, robot positions\, team coverage\, and remaining budget. The formulation covers single robots\, homogeneous UGV teams\, and heterogeneous UAV-UGV teams. QUEST reaches 0.913 F-score with four UGVs versus 0.853 for the strongest learned baseline\, maintains its lead under communication outage and two simultaneous mid-mission robot failures without retraining\, and on out-of-distribution UAV-UGV compositions improves early F-score\, 0.759 versus 0.728\, while using 10% less path length and 12% less duplicate coverage. A map-structure analysis using algebraic connectivity and modularity ties these gains to the topology of the search graph\, predicting when non-myopic value learning is and is not worth its training cost. \nTogether\, these policies connect field deployment with coordinated team search that generalizes across composition\, communication\, and partial failure under one shared-belief active-search formulation. \n\nCommittee:\nDr. Jeff Schneider (chair)\nDr. Wennie Tabib\nDr. Tejus Gupta
URL:https://www.ri.cmu.edu/event/explore-and-exploit-learning-policies-for-efficient-and-coordinated-active-search/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260526T103000
DTEND;TZID=America/New_York:20260526T113000
DTSTAMP:20260707T193018
CREATED:20260520T151702Z
LAST-MODIFIED:20260520T151702Z
UID:151374-1779791400-1779795000@www.ri.cmu.edu
SUMMARY:Toward Real-World Autonomous Off-Road Driving with Reinforcement Learning
DESCRIPTION:Abstract:\nOff-road autonomous driving presents significant challenges such as navigating unmapped variable environments\, traversing difficult terrain geometries such as steep slopes and ditches\, and managing complex terrain dynamics. Addressing these challenges requires effective low-level adaptable control and long-horizon planning. Most existing methods utilize Model Predictive Control (MPC) methods such as Model Predictive Path Integral (MPPI)\, which have long-horizon planning capabilities\, but require expensive dense sampling and precise dynamics modeling that are impractical to deploy for real-time control. On the other hand\, Reinforcement Learning (RL) learns complex dynamics and reactive low-level control policies directly from interaction\, but typically fails to plan and navigate dense environments due to poor exploration.\n\nTo utilize the different strengths of both MPC methods and RL\, this thesis proposes a hierarchical pipeline with a low-frequency high-level MPPI planner for long-horizon planning and a high-frequency low-level adaptable RL controller. In order to solve RL’s poor exploration problem\, this thesis introduces Teacher Action Distillation Policy Optimization (TADPO)\, a novel teacher-student policy gradient formulation that extends Proximal Policy Optimization (PPO) to guide policy learning\, leveraging off-policy teacher trajectories for teacher guidance and on-policy trajectories for student exploration.\n\nWith TADPO\, we develop a vision-based\, end-to-end RL policy and off-road autonomy system for high-speed driving\, capable of navigating extreme slopes and obstacle-dense terrain. Firstly\, we demonstrate our performance in simulation and\, more importantly\, zero-shot sim-to-real transfer on a full-scale off-road vehicle. Then\, we show further performance improvements of the deployed policy in simulation. Finally\, we illustrate TADPO’s generalizability with a policy that navigates through trees in simulation. To our knowledge\, this work represents the first deployment of RL-based policies on a full-scale off-road robotics platform. \nCommittee:\nJeff Schneider (advisor)\nJohn Dolan\nSamuel Triest
URL:https://www.ri.cmu.edu/event/toward-real-world-autonomous-off-road-driving-with-reinforcement-learning/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260522T153000
DTEND;TZID=America/New_York:20260522T170000
DTSTAMP:20260707T193018
CREATED:20260514T153637Z
LAST-MODIFIED:20260514T153734Z
UID:151265-1779463800-1779469200@www.ri.cmu.edu
SUMMARY:RI PhD Thesis Defense - Kenneth Shaw
DESCRIPTION:Date: Friday May 22\nTime: 3:30PM – 4:30PM (EST)\nLocation: NSH (Newell Simon Hall) 3305 \nZOOM Link \nTitle: Building Robot Hands and Teaching Dexterity \nAbstract:  \nOur human hands are masterpieces of power and precision\, capable of typing\, hammering\, or delicately using chopsticks. Yet most robots today still rely on simple two-finger grippers in controlled settings because dexterous hands are costly and difficult to deploy. To close this gap\, I will introduce my LEAP Hands\, high-performance\, low-cost\, and easy-to-assemble robotic hands that have become the most widely used platform for dexterous manipulation research. LEAP Hand V1 employs motor-in-joint actuation for simplicity\, while V2 introduces a novel hybrid rigid–soft structure that delivers exceptional strength and durability.  I will then show how large-scale human video/motion data and simulation techniques can teach human-like manipulation skills across diverse environments.  By tightly integrating mechanical design and machine learning\, my open-source robot hands achieve unprecedented levels of dexterity for a variety of everyday tasks.  To conclude\, I will discuss emerging directions in the field of dexterous manipulation research and share my future vision that I will pursue as a faculty member at the University of Illinois. \n​Prof. Deepak Pathak (advisor) \nProf. Nancy Pollard \nProf. Abhinav Gupta \nProf. Jitendra Malik\, UC Berkeley \nDr. Ankur Handa\, NVIDIA \n  \nThesis Draft
URL:https://www.ri.cmu.edu/event/ri-phd-thesis-defense-kenneth-shaw/
LOCATION:Newell-Simon Hall 3305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260521T153000
DTEND;TZID=America/New_York:20260521T170000
DTSTAMP:20260707T193018
CREATED:20260511T201231Z
LAST-MODIFIED:20260511T201231Z
UID:151239-1779377400-1779382800@www.ri.cmu.edu
SUMMARY:RI PhD Thesis Proposal - Anurag Ghosh
DESCRIPTION:Date: May 21st\, 2026\nTime: 3:30 – 5:00 pm\nRoom: NSH Room 4305\nZoom:  https://cmu.zoom.us/j/98318417145 \nType: RI PhD Thesis Proposal\nWho: Anurag Ghosh\n\n\nTitle: Scaling Long-Tailed Driving Perception and Planning with In-the-Wild Videos\n\n\nAbstract: Closed-loop driving\, where methods produce actions a simulator reacts to\, remains largely tied to driving logs from instrumented fleets. Thus\, reliably driving in rare-but-critical scenarios is still elusive. Meanwhile\, foundation models are increasingly common in autonomous driving\, vision-language and video world models are tackling open-loop tasks like scene description and video generation. Although the internet has revolutionized language and image generation\, planning in autonomous driving has not seen its ImageNet moment yet. Therefore\, an opportunity exists to leverage internet-scale data and tackle long-tailed autonomous driving.\n\nWe focus on work zones as a representative long-tail scenario as they are a major source of disengagements for commercial systems today. Work zones uniquely combine rare objects (e.g.\, construction vehicles\, arrow boards)\, unusual layouts (e.g.\, temporary closures\, crossing yellow lines)\, and unpredictable behaviors (e.g.\, flaggers\, sudden merges). These safety-critical scenarios are considered difficult to simulate at scale. \nFirst\, we mine long-tailed driving videos from a massive corpus. We find that foundation models fail at work zone perception and fine-tuning on our data combined with simple priors makes them effective. Second\, we introduce a resource-efficient\, geometry-based prior that improves scene perception and long-tail object detection. Third\, we focus on long-tailed closed-loop planning and develop an anytime language-action planner capable of real-time trajectory generation and contextual textual reasoning. Furthermore\, by developing a novel rules-based planner that effectively handles current benchmark scenarios\, we show that existing closed-loop driving benchmarks are insufficient for evaluating long-tailed behaviors. \nFinally\, this thesis proposes a framework that\, by carefully composing geometry-aware methods\, street-view imagery\, and foundation models\, lifts monocular videos into metric\, geo-referenced 4D driving logs compatible with existing simulators. Using this framework\, we create a new long-tail planning benchmark and propose to uncover insights and study the geographic scaling behavior of state-of-the-art planning methods. \nUltimately\, to advance autonomous driving beyond fleets\, we argue scaling of training and evaluation is achievable by harnessing internet-scale data while grounding foundation models with geometric and physical priors.\n\nCommittee:\nSrinivasa Narasimhan\, Chair\nDeva Ramanan\nMaxim Likhachev\nChristoph Mertz\nManmohan Chandraker\, UC San Diego\n\nThesis Proposal Draft
URL:https://www.ri.cmu.edu/event/ri-phd-thesis-proposal-anurag-ghosh/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Proposal,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260521T123000
DTEND;TZID=America/New_York:20260521T133000
DTSTAMP:20260707T193018
CREATED:20260515T135220Z
LAST-MODIFIED:20260515T135220Z
UID:151275-1779366600-1779370200@www.ri.cmu.edu
SUMMARY:Generalizable Neural Dynamics Modeling for Complex Deformable Object Manipulation
DESCRIPTION:Abstract:\nHair care is an essential daily activity\, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous robot systems due to the fine-grained physical structure and complex dynamics of hair.\nThis thesis presents DYMO-Hair\, a model-based robot hair care system. We introduce a novel dynamics learning paradigm that is suited for volumetric quantities such as hair\, relying on an action-conditioned latent state editing mechanism\, coupled with a compact 3D latent space of diverse hairstyles to improve generalizability. This latent space is pre-trained at scale using a novel hair physics simulator\, enabling generalization across previously unseen hairstyles. Using the dynamics model with a Model Predictive Path Integral (MPPI) planner\, DYMO-Hair is able to perform multimodal goal-conditioned hair styling\, supporting both visual references and natural language instructions as goal configurations.\nExperiments in simulation demonstrate that DYMO-Hair’s dynamics model outperforms baselines on capturing local deformation for diverse\, unseen hairstyles. DYMO-Hair further outperforms baselines in closed-loop visual goal-conditioned hair styling tasks on unseen hairstyles\, with an average of 22% lower final geometric error and 42% higher success rate than the state-of-the-art system. Real-world experiments exhibit zero-shot transferability of our system to wigs\, achieving consistent success on challenging unseen hairstyles where the state-of-the-art system fails. We further conduct a user study to evaluate the system’s language goal-conditioned styling capability\, suggesting improved goal achievement and progress consistency over zero-shot VLM-based methods.\nTogether\, these results introduce a foundation for model-based robot hair care\, advancing toward more generalizable\, flexible\, and accessible robot hair styling in unconstrained physical environments.\n\nCommittee:\nProf. Jean Oh (Chair)\nProf. Jeffrey Ichnowski\nProf. David Held\nUksang Yoo
URL:https://www.ri.cmu.edu/event/generalizable-neural-dynamics-modeling-for-complex-deformable-object-manipulation/
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260521T103000
DTEND;TZID=America/New_York:20260521T113000
DTSTAMP:20260707T193018
CREATED:20260518T135147Z
LAST-MODIFIED:20260518T135147Z
UID:151279-1779359400-1779363000@www.ri.cmu.edu
SUMMARY:Scalable Imitation Learning for Robust Manipulation and Physical Human-Robot Interaction
DESCRIPTION:Abstract:\nRobots in everyday human environments are expected to perform robust manipulation across cluttered\, constrained\, and physically interactive settings. This thesis studies how scalable simulation-based data generation can train vision-based imitation learning policies for robust zero-shot transfer to the real world. \nIn the first part of this thesis\, we study robotic manipulation in cluttered shelf environments\, where limited free space\, dense object arrangements\, and complex visual backgrounds make policy learning challenging. We present a scalable learning pipeline that leverages Dynamic Movement Primitives (DMPs) to expand a small set of teleoperated demonstrations into a large and diverse synthetic dataset. We train an imitation learning policy across six scenarios and three manipulation strategies\, demonstrating robust generalization across diverse object shapes and zero-shot transfer to a physical manipulator.\nIn the second part of this thesis\, we address autonomous physical human-robot interaction (pHRI)\, where large-scale real-world training data is difficult to collect. We introduce a zero-shot “text2sim2real” generative simulation framework that synthesizes diverse pHRI scenarios from high-level natural-language prompts\, including soft-body human models\, scene layouts\, and robot motion trajectories. Using the generated demonstrations\, we train vision-based imitation learning policies on segmented point clouds and show zero-shot sim-to-real transfer on scratching and bathing tasks\, with success rates exceeding 80%.\nTogether\, these two works demonstrate that scalable simulation data generation can serve as a practical foundation for robust robot policies in both constrained manipulation and physical human-robot interaction.\n\nCommittee:\nProf. Zackory Erickson\, chair\nProf. David Held\nYufei Wang
URL:https://www.ri.cmu.edu/event/scalable-imitation-learning-for-robust-manipulation-and-physical-human-robot-interaction/
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260520T150000
DTEND;TZID=America/New_York:20260520T163000
DTSTAMP:20260707T193018
CREATED:20260512T145730Z
LAST-MODIFIED:20260512T145730Z
UID:151245-1779289200-1779294600@www.ri.cmu.edu
SUMMARY:RI PhD Thesis Proposal - Renos Zabounidis
DESCRIPTION:Date: May 20th\, 2026\nTime: 3:00 – 4:30 pm\nLocation: NSH 4305\nZoom Link \nType: RI PhD Thesis Proposal\nWho: Renos Zabounidis \nTitle: Enforcing Neuro-Symbolic Structure in Deep Reinforcement Learning \nAbstract: Monolithic deep reinforcement learning trains a single network to learn vision\, physics\, planning\, and control from reward alone. The result is poor sample efficiency\, brittle generalization\, and uninterpretable decisions. This thesis shows how to build domain knowledge into policy architecture and enforce these architectural priors during training. \nWe develop this claim at three levels of abstractions. Concept-level abstractions route predictions through human-interpretable predicates such as `door_present’ and `key_in_inventory’\, enabling runtime inspection and intervention. Action-level constraints enforce state-dependent action validity\, preventing unmasked training from suppressing rarely valid behaviors through shared representations. Compositional abstractions represent long-horizon tasks as reusable symbolic skills that a planner can sequence while RL grounds each skill in low-level control. \nBuilding on these foundations\, this proposal focuses on two future directions. Concept-conditioned latent action models impose semantic structure on variational motor representations\, allowing high-level controllers to sample behaviors by name. A planning-guided option critic learns dynamic skill scheduling under precondition constraints\, replacing static plan traversal with on-policy option selection. \nTogether\, these contributions show that domain knowledge in the policy architecture reduces sample complexity\, enables cross-task skill composition\, and makes internal decisions available for inspection and override. \nThesis Committee:\nKatia Sycara\, CMU (Chair)\nSebastian Scherer\, CMU\nYonatan Bisk\, CMU\nKevin Ellis\, Cornell University \n\nThesis URL
URL:https://www.ri.cmu.edu/event/ri-phd-thesis-proposal-renos-zabounidis/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Proposal,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260519T130000
DTEND;TZID=America/New_York:20260519T150000
DTSTAMP:20260707T193018
CREATED:20260514T173912Z
LAST-MODIFIED:20260514T173912Z
UID:151271-1779195600-1779202800@www.ri.cmu.edu
SUMMARY:RI PhD Thesis Defense - Junyu Nan
DESCRIPTION:Date: Tuesday May 19\, 2026\nTime:  1:00 – 3:00PM (EST)\nLocation: NSH (Newell Simon Hall) 3305 \nZoom Link \nType: PhD Thesis Defense \nTitle: Learning Geometric\, Physical\, and Semantic Priors for Embodied Planning and Control \nAbstract: Embodied intelligence requires perceiving\, predicting\, and acting in environments with an understanding of the geometric\, semantic\, and physical structure of the world. Recently\, the dominant trend in robotics has been to acquire such world understanding implicitly in a data-driven manner using end-to-end models. While these approaches have achieved impressive milestones\, they often rely on substantial amounts of in-domain data and may remain brittle when success depends on long-horizon reasoning\, precise physical interaction\, or generalization from limited task-specific data. This thesis studies a perspective complementary to end-to-end approaches: when aspects of geometric\, physical\, and semantic structure are known and reusable\, explicitly learning priors over them can improve the data efficiency\, fidelity\, and robustness of embodied learning systems. \nThis thesis instantiates this perspective across four embodied settings: scene-level forecasting in autonomous driving\, learning deformable object dynamics from robot interaction videos\, relational reasoning and cross-instance manipulation transfer\, and zero-shot long-horizon manipulation. For scene-level prediction\, we learn a predictive geometric prior over the future evolution of the full 3D scene representation. By modeling future motion at the scene level\, the predictive geometric representation preserves coherence across agents and the environment\, improving downstream prediction and planning under multi-agent uncertainty. Moving from passive scene forecasting to manipulation\, physical priors become important to predict how state evolves in response to robot actions. To learn contact-rich dynamics and topological change directly from RGB-D robot interaction videos\, we represent deformable objects as adaptive sets of 3D Gaussians inside a particle-filtering framework with physics-inspired interaction modeling and resampling mechanisms. Extending to multi-object manipulation problems\, we develop semantic priors based on correspondence as a reusable representation for relational reasoning and cross-instance manipulation. These correspondence-based priors allow a robot to identify functionally meaningful object structure\, reason about object alignment under geometric ambiguity\, and transfer manipulation knowledge from demonstrated objects to novel instances. Finally\, we integrate learned geometric\, semantic\, and physical priors into a zero-shot long-horizon manipulation system that connects high-level video and language planning with executable robot motion through geometric grounding of generated videos. Taken together\, these works show that explicitly learned geometric\, physical\, and semantic priors can improve the data efficiency\, fidelity\, and robustness of embodied prediction\, planning\, and manipulation systems. \nCommittee: \nKris Kitani (Chair)\, Carnegie Mellon University \nDavid Held\, Carnegie Mellon University \nShubham Tulsiani\, Carnegie Mellon University \nBrian Okorn\, Robotics and AI Institute \nThesis Draft
URL:https://www.ri.cmu.edu/event/ri-phd-thesis-defense-junyu-nan/
LOCATION:Newell-Simon Hall 3305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260518T130000
DTEND;TZID=America/New_York:20260518T150000
DTSTAMP:20260707T193018
CREATED:20260319T204042Z
LAST-MODIFIED:20260319T204042Z
UID:150640-1779109200-1779116400@www.ri.cmu.edu
SUMMARY:RI PhD Speaking Qualifier / MSR Talk - Yuemin Mao
DESCRIPTION:TBD
URL:https://www.ri.cmu.edu/event/ri-phd-speaking-qualifier-msr-talk-yuemin-mao/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Speaking Qualifier,Student Talks
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END:VCALENDAR