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X-WR-CALNAME:Robotics Institute Carnegie Mellon University
X-ORIGINAL-URL:https://www.ri.cmu.edu
X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
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DTSTART;TZID=America/New_York:20251202T110000
DTEND;TZID=America/New_York:20251202T120000
DTSTAMP:20260922T070259
CREATED:20251125T145824Z
LAST-MODIFIED:20251125T145824Z
UID:149623-1764673200-1764676800@www.ri.cmu.edu
SUMMARY:Towards Scaling Embodied Data for Robot Learning
DESCRIPTION:Abstract:\nAs artificial intelligence advances quickly in the digital domain\, the next\nfrontier lies in physical intelligence: systems that learn through acting and\nsensing in the real world. In this thesis\, we explore practical ways of scaling\nsuch embodied data across three directions. AnyCar scales synthetic data\nthrough large-scale simulation\, training a universal dynamics transformer\nthat generalizes across vehicles and environments. FACTR improves\nthe efficiency of real robot data with a low-cost bilateral teleoperation\nsystem and a curriculum that teaches policies to integrate force and\nvision. DexWild scales human data through in-the-wild data collection\nand co-training with robot demonstrations\, enabling generalization to\nunseen objects and environments. Together\, these projects explore how a\ndata-centric approach can enable more adaptive and capable robots. \nCommittee:\nDeepak Pathak (chair)\nGuanya Shi\nKenneth Shaw
URL:https://www.ri.cmu.edu/event/towards-scaling-embodied-data-for-robot-learning/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251203T093000
DTEND;TZID=America/New_York:20251203T103000
DTSTAMP:20260922T070259
CREATED:20251201T142655Z
LAST-MODIFIED:20251201T142655Z
UID:149640-1764754200-1764757800@www.ri.cmu.edu
SUMMARY:Attractors and Their Applications in Heuristic Search
DESCRIPTION:Abstract:\nHeuristic search provides a principled way to guide exploration in large state spaces\, enabling efficient solution finding. As a result\, it is widely used across domains such as robotics\, games\, and planning. However\, its performance is often limited by memory consumption and computational overhead\, which have motivated extensive research on improving both. This thesis introduces a sparse representation called attractors and explores two of its applications in heuristic search. First\, we present Attractor-based Closed List Search (ACLS)\, a framework that uses attractors to sparsely represent the Closed list. ACLS intelligently identifies attractor states in a way that enables efficient solution reconstruction while preserving theoretical guarantees on the quality of the solution. We demonstrate that ACLS significantly reduces memory usage\, while achieving comparable planning times and outperforming state-of-the-art approaches. Second\, we introduce front-to-attractors (F2A) heuristics\, a family of heuristics that leverage attractors in bidirectional heuristic search (Bi-HS). We demonstrate that F2A heuristics substantially reduce the number of heuristic evaluations compared to front-to-front (F2F) heuristics\, while maintaining strong informativeness and reducing expansions relative to front-to-end (F2E) heuristics\, resulting in improved runtime performance. Together\, these projects demonstrate the broad potential of attractors in heuristic search.\n\nCommittee:\nMaxim Likhachev (chair)\nJiaoyang Li\nYorai Shaoul
URL:https://www.ri.cmu.edu/event/attractors-and-their-applications-in-heuristic-search/
LOCATION:GHC 4405
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251203T150000
DTEND;TZID=America/New_York:20251203T163000
DTSTAMP:20260922T070259
CREATED:20251125T221309Z
LAST-MODIFIED:20251125T221309Z
UID:149634-1764774000-1764779400@www.ri.cmu.edu
SUMMARY:Robotic System Design Principles for Human-Human Collaboration
DESCRIPTION:Abstract: \nRobots possess unique affordances granted by combining software and hardware. Most existing research focuses on the impact of these affordances on human-robot collaboration\, but the theory of how robots can facilitate human-human collaboration is underdeveloped. Such a theory would be beneficial in education. An educational device can afford collaboration in both assembly and use. This thesis will enumerate and validate the design principles of educational devices that facilitate collaborative assembly and collaborative learning.\nThis research draws upon cognitive theories used in the disciplines of Computer-Supported Collaborative Work (CSCW)\, Computer-Supported Collaborative Learning (CSCL)\, Educational Robotics\, and Human-Robot Interaction (HRI). Each discipline uses theories that align with its respective goals to model different pieces of cognition. However\, they do not consider other factors outside their respective goals. Diverse analytical lenses are needed to understand the multiple dimensions of influence an educational device can have on human-human interaction to support collaborative assembly and collaborative learning.\nWe explore these dimensions first through the development and assessment of\nRoboLoom\, a robotic Jacquard loom kit designed for interdisciplinary\, collaborative education. Through the study of RoboLoom’s use and assembly in an undergraduate course\, we extract design features that facilitate student-student collaboration during classroom activities. These features encompass task complexity\, task parallelization\, physicality\, repetition of tasks\, specificity of hardware\, and familiarity with hardware.\nWe then explore these design principles through three studies: a comparison\nbetween two different looms\, a study of devices designed for and against the principles\, and a comparison of two versions of RoboLoom. We find five design principles that influence collaborative behavior: repetitiveness\, specificity\, parallelizability\, physicality\, and difficulty. These design principles were shown to causally change collaborative behaviors in controlled lab settings and in situ engineering education tasks. By evaluating these systems through multiple cognitive lenses\, we determine that these design principles are effective in facilitating collaborative assembly and promising for collaborative learning.\n\n\n\nCommittee Members: \n    Illah Nourbakhsh\, Co-Chair\n    Melisa Orta Martinez\, Co-Chair\nJames McCann\nKylie Peppler\, University of California\, Irvine \n\nLink to Thesis
URL:https://www.ri.cmu.edu/event/robotic-system-design-principles-for-human-human-collaboration/
LOCATION:GHC 8102
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251208T170000
DTEND;TZID=America/New_York:20251208T183000
DTSTAMP:20260922T070259
CREATED:20250929T144749Z
LAST-MODIFIED:20250929T144749Z
UID:148939-1765213200-1765218600@www.ri.cmu.edu
SUMMARY:Erica Weng - PhD Defense Info TBA
DESCRIPTION:More info coming soon
URL:https://www.ri.cmu.edu/event/erica-weng-phd-defense-info-tba/
LOCATION:NSH 4305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251209T120000
DTEND;TZID=America/New_York:20251209T133000
DTSTAMP:20260922T070259
CREATED:20251125T211907Z
LAST-MODIFIED:20251125T211907Z
UID:149629-1765281600-1765287000@www.ri.cmu.edu
SUMMARY:Design Optimization of Modular Manipulators for Manipulation in Cluttered Agricultural Environments
DESCRIPTION:Abstract:\nAlthough agriculture is a highly mechanized industry\, essential and high-value subsectors such as horticulture and floriculture remain heavily reliant on manual labor because they require complex\, contact-rich\, and highly selective handling of both plants and produce. The variability and density of tree-canopy clutter further complicate the automation process\, making robot performance difficult to quantify consistently and preventing the development of a single\, universally effective automation solution. Modular and reconfigurable robots (MRRs) can help address this challenge by reducing the cost of creating custom robots tailored to specific task requirements. However\, determining the optimal robot design configuration for an MRR system remains a complex and unintuitive process\, even for experts. This thesis addresses the problem of automating the robot design process by introducing a systematic design framework that unifies deterministic and consistent task-performance metrics with global optimization methods primarily targeting agricultural manipulation tasks. \nThe first contribution targets the challenge of computing self-motion manifolds (SMMs)\, which are global inverse-kinematics solutions for redundant manipulators. We solve this problem using Runge-Kutta solvers after posing the underlying ordinary differential equation problem in a form we call the SMM Initial Value Problem (SMM-IVP). The SMM-IVP is able to trace the manipulator’s self-motion configuration space reliably. Compared to existing predictor-corrector and linear step-corrector approaches\, the SMM-IVP exhibits improved convergence behavior and numerical stability. For design applications\, the SMM-IVP acts as a global inverse-kinematics procedure that provides consistent and initialization-independent performance characterization\, which is essential for design optimization in the cluttered conditions typical of agricultural manipulation. \nBuilding on the first contribution\, the second contribution develops a general framework for formulating and solving robot-design optimization problems. In parallel\, we introduce new SMM-based performance metrics that more accurately characterize dexterity metrics for redundant manipulators. We apply this framework and the new metrics to a manipulator placement optimization problem for a dual-arm pepper-harvesting system\, and we show that it produces highly performant\, non-intuitive configurations that outperform both human-expert designs and conventional dexterity-based baselines. \nThe third contribution grounds our design methods in real-world tree geometry data and directly addresses the inherent heterogeneity of robot performance. To this end\, we introduce a lexicographic design optimization framework for tuple-valued task metrics\, allowing robot performance to be represented as a set of multiple criteria ordered according to designer-specified priorities. This representation preserves the semantic meaning of each criterion\, enables explicit hierarchical prioritization\, and provides a principled alternative to ad-hoc scalarization methods. \nTogether\, these advances establish a reproducible foundation for task-driven robot design optimization. The methods integrate kinematic modeling\, performance evaluation\, and global optimization into a single\, coherent pipeline that extends beyond agricultural manipulation. More broadly\, this work supports the practical deployment of modular\, reconfigurable manipulators by lowering the barriers to designing task-specific robot designs for the highly cluttered conditions like those found in agricultural robot manipulation. \nThesis Committee Members:\nGeorge Kantor (Chair)\, CMU\nOliver Kroemer\, CMU\nZeynep Temel\, CMU\nChangying (Charlie) Li\, University of Florida \nThesis Draft
URL:https://www.ri.cmu.edu/event/design-optimization-of-modular-manipulators-for-manipulation-in-cluttered-agricultural-environments/
LOCATION:Newell-Simon Hall 3305
CATEGORIES:PhD Thesis Defense,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251212T110000
DTEND;TZID=America/New_York:20251212T120000
DTSTAMP:20260922T070259
CREATED:20251208T224116Z
LAST-MODIFIED:20251208T224116Z
UID:149685-1765537200-1765540800@www.ri.cmu.edu
SUMMARY:Examining Engagement and Motivation in a Conversational Robotic Exercise Coach for Older Adults
DESCRIPTION:Abstract: Exercise is essential for healthy aging\, but motivation and adherence to exercise often decline with age\, leading to a more sedentary lifestyle. At the same time\, the growing aging population continues to strain the availability of physical therapists and exercise coaches. In this thesis\, we introduce a conversational robotic exercise coach system designed to support older adults during exercise. To evaluate this system\, we conducted a user study with 10 participants aged 59 and above. We analyzed both survey responses and verbal interactions to understand how participants engaged with the robot and how motivation was expressed during exercise. Based on these findings\, this work presents design recommendations for future autonomous conversational exercise robots for older adults. \nCommittee: \nProf. Aaron Steinfeld (advisor) \nProf. Reid Simmons \nProf. Jean Oh \nMichelle Zhao
URL:https://www.ri.cmu.edu/event/examining-engagement-and-motivation-in-a-conversational-robotic-exercise-coach-for-older-adults/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,PhD Speaking Qualifier,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251212T110000
DTEND;TZID=America/New_York:20251212T120000
DTSTAMP:20260922T070259
CREATED:20251209T143621Z
LAST-MODIFIED:20251209T143621Z
UID:149688-1765537200-1765540800@www.ri.cmu.edu
SUMMARY:Examining Engagement and Motivation in a Conversational Robotic Exercise Coach for Older Adults
DESCRIPTION:Abstract: Exercise is essential for healthy aging\, but motivation and adherence to exercise often decline with age\, leading to a more sedentary lifestyle. At the same time\, the growing aging population continues to strain the availability of physical therapists and exercise coaches. In this thesis\, we introduce a conversational robotic exercise coach system designed to support older adults during exercise. To evaluate this system\, we conducted a user study with 10 participants aged 59 and above. We analyzed both survey responses and verbal interactions to understand how participants engaged with the robot and how motivation was expressed during exercise. Based on these findings\, this work presents design recommendations for future autonomous conversational exercise robots for older adults. \nCommittee: \nProf. Aaron Steinfeld (advisor) \nProf. Reid Simmons \nProf. Jean Oh \nMichelle Zhao
URL:https://www.ri.cmu.edu/event/examining-engagement-and-motivation-in-a-conversational-robotic-exercise-coach-for-older-adults-2/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251212T150000
DTEND;TZID=America/New_York:20251212T163000
DTSTAMP:20260922T070259
CREATED:20251202T210118Z
LAST-MODIFIED:20251202T231025Z
UID:149656-1765551600-1765557000@www.ri.cmu.edu
SUMMARY:Modeling what Matters: Emergent Abstraction In Reinforcement Learning
DESCRIPTION:Abstract: Real-world decision-making is rife with partial observability\, long horizons\, and complex multi-agent interactions. This thesis argues that abstraction—forming simplified representations of the task that retain relevant information—offers a unifying principle for tackling these challenges across model-free and model-based reinforcement learning (RL). We develop methods in which abstractions are not hand-designed but emerge from learning objectives\, yielding representations that improve an agent’s ability to cope with high-dimensional observations\, extended temporal dependencies\, and inter-agent coupling.\n\nOn the model-free\, multi-agent side\, we introduce Partial Reward Decoupling (PRD)\, a game-abstraction mechanism that dynamically decomposes teams into subgroups\, simplifying cross-agent credit assignment and accelerating cooperative learning. We also study discrete communication learning under bandwidth constraints\, where agents learn what information to transmit\, to whom\, and how to encode it—linking communication learning to representation learning and generative modeling. \nWe also show how abstraction mitigates the misalignment between model-learning and task objectives typically found in model-based RL methods. By focusing limited model capacity on task-relevant factors and operating at an appropriate temporal scale\, abstraction improves the utility of world models for decision-making. Toward this end\, we explore the use of variational inference (VI) to learn both state and temporal abstractions. We demonstrate a state-abstraction method that ignores distracting details while retaining task-relevant features\, attaining strong results on distraction-rich control benchmarks without relying on data-augmentation heuristics. We also propose a latent-variable approach to temporal abstraction that extracts skills and learns a temporally abstract dynamics model from offline data\, enabling effective long-horizon prediction and planning for downstream tasks. \nFinally\, we present Unified RL\, which blends model-based and model-free updates by detecting when a learned model ceases to be useful for policy improvement and falling back to model-free learning updates. Empirically\, Unified RL retains the data efficiency of model-based methods while achieving asymptotic performance comparable to model-free RL. \nCommittee Members:  \nHowie Choset\, chair\nJeff Schneider\, co-chair\nRuslan Salakhutdinov\nRoberto Calandra\, TU Dresden\n \nLink to thesis
URL:https://www.ri.cmu.edu/event/modeling-what-matters-emergent-abstraction-in-reinforcement-learning/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
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