Calendar of Events
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2 events,
PhD Thesis Defense
Cherie Ho
Flexible Perception for High-Performance Robot Navigation
Abstract: Real-world autonomy requires perception systems that deliver rich, accurate information given the task and environment. However, as robots scale to diverse and rapidly evolving settings, maintaining this level of performance becomes increasingly brittle and labor-intensive, requiring significant human engineering and retraining for even small changes in environment and problem definition. To overcome this bottleneck, […]
VASC Seminar
Hong-Xing “Koven” Yu
Computer Science Department , Stanford University
Generating a Physical World
Abstract: Generating an interactive, enlivened, and physical world enables a wide range of applications in entertainment, embodied AI, education, and creative designs. Recent image/video models have shown promise in producing realistic visuals, yet they operate purely at the pixel level and lack underlying physical grounding, leading to failures in physical fidelity and user interactivity. In […]
1 event,
PhD Thesis Proposal
Tejus Gupta
Learning Bayesian Experimental Design Policies Efficiently and Robustly
Abstract: Bayesian Experimental Design (BED) provides a principled framework for sequential data-collection under uncertainty, and is used in a wide set of domains such as clinical trials, ecological monitoring, and hyperparameter optimization. Despite its wide applicability, BED methods remain challenging to deploy in practice due to their significant computational demands. This thesis addresses these computational […]
1 event,
PhD Thesis Proposal
Shibo Zhao
Unlocking Robust Spatial Perception: Resilient State Estimation and Mapping for Long-term Autonomy
Abstract: How can we enable robots to perceive, adapt, and understand their surroundings like humans—in real-time and under uncertainty? Just as humans rely on vision to navigate complex environments, robots need robust and intelligent perception systems—“eyes” that can endure sensor degradation, adapt to changing conditions, and recover from failure. However, today’s visual systems are fragile—easily […]
1 event,
VASC Seminar
David Chu
NVIDIA
When Spatial Computing meets Accelerated Computing
Abstract: NVIDIA has been pioneering Accelerated Computing for the past three decades, driving innovations that have transformed society. Among all personal computing mediums, Spatial Computing and Extended Reality (XR) stand out as some of the most promising beneficiaries of accelerated computing. In this talk, we will explore the latest developments and trends in the XR ecosystem, […]
2 events,
PhD Thesis Proposal
Mosamkumar Dabhi
From Pixels to Physical Intelligence: Semantic 3D Data Generation at Internet Scale
Abstract: Modern AI won’t achieve physical intelligence until it can extract rich, semantic spatial knowledge from the wild ocean of internet video—not just curated motion-capture datasets or expensive 3D scans. This thesis proposes a self-bootstrapping pipeline for converting raw pixels into large-scale 3D and 4D spatial understanding. It begins with multi-view bootstrapping: using just two […]
PhD Thesis Proposal
Akash Sharma
Self supervised perception for Tactile Dexterity
Abstract: Humans are incredibly dexterous. We interact with and manipulate tools effortlessly, leveraging touch without giving it a second thought. Yet, replicating this level of dexterity in robots, is a major challenge. While the robotics community, recognizing the importance of touch in fine manipulation, has developed a wide variety of tactile sensors, how best to […]
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2 events,
PhD Thesis Proposal
Ruixuan Liu
Prompt-to-Product: Generative Assembly via Bimanual Manipulation
Abstract: Assembly products are ubiquitous in our lives, for example, chairs, tables, couches, drawers, and more. Due to the complex interactions between components, creating such products typically demands significant manual effort in 1) designing the assembly and 2) constructing the product. This thesis seeks to reduce the required manual effort by automating the creation process […]
PhD Thesis Proposal
Mohamad Qadri
Differentiable Probabilistic Inference and Rendering for Multimodal Robotic Perception
Abstract: Robots are increasingly deployed to automate tasks that are dangerous or mundane for humans such as search and rescue, mapping, and inspection in difficult environments. They rely on their perception stack, typically composed of complementary sensing modalities, to estimate their own state and the state of the environment to enable informed decision-making. This thesis […]
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1 event,
PhD Thesis Defense
Homanga Bharadhwaj
Watch, Predict, Act: Robot Learning meets Web Videos
Abstract: To enable robots to assist in everyday tasks in diverse natural environments such as homes, offices, and kitchens, it is critical to develop policies that generalize to novel tasks in unseen scenarios. Practical utility demands that these policies do not require task-specific adaptation at test time but can instead execute directly given a natural […]
0 events,
0 events,
0 events,
1 event,
MSR Thesis Defense
Srujan Deolasee
Towards Robust Informative Path Planning for Spatiotemporal Environment Prediction
Abstract: Informative Path Planning (IPP) is an important planning paradigm for various real-world robotic applications such as wildfire monitoring and predicting infection spread in crops. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering to planning constraints. Traditional IPP methods are effective only in static, time-invariant […]
2 events,
PhD Thesis Proposal
Chung Hee Kim
Semantics-Driven Perception and Manipulation for Agricultural Robotics
Abstract: With growing expectations for autonomous robot deployment in unstructured, real-world environments, these systems must operate efficiently while perceiving and interpreting complex scenes to navigate dynamic, cluttered conditions. Robust performance in these settings require handling occlusions, clutter, and ambiguous visual cues; challenges exacerbated by the limited semantic understanding in standard visuomotor policy frameworks. This thesis […]
MSR Thesis Defense
Yulong Li
Towards Dexterous Robotic Manipulation by Imitating Experts
Abstract: Imitation learning enables scalable transfer of complex manipulation skills to robots, but its effectiveness depends on high-quality demonstrations and robust policy learning, especially in dynamic, contact-rich environments. This thesis investigates how combining imitation learning with teleoperation and classical planners can teach dexterous manipulation across diverse real-world settings. We develop a teleoperation system for collecting […]
1 event,
PhD Thesis Proposal
Junyu (Jenny) Nan
Unified Predictive Representations for Generalized Robotic Perception
Abstract: Building robots that can perceive, reason, and act across a wide range of objects and environments remains a central goal in robotics. To achieve such generalization without relying on large amounts of task-specific data, predicting future outcomes in response to actions is a core capability towards generalized robotics. In this thesis, we investigate how to […]
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2 events,
Faculty Events
Guanya Shi
Structured and Adaptive Real2Sim2Real RL for Humanoid Whole-Body Control and Loco-Manipulation
Abstract: Humanoid robots offer two unparalleled advantages in general-purpose embodied intelligence. First, humanoids are built as generalist robots that can potentially do all the tasks humans can do in complex environments. Second, the embodiment alignment between humans and humanoids allows for the seamless integration of human cognitive skills with versatile humanoid capabilities. To fully unleash the […]
MSR Thesis Defense
Renos Zabounidis
Enhancing Concept-Based Decision Making in AI Models with Disentanglement
Abstract: Deploying AI in high-stakes settings requires models that are not only accurate but also interpretable and amenable to human oversight. Concept Bottleneck Models (CBMs) support these goals by structuring predictions around human-understandable concepts, enabling interpretability and post-hoc human intervenability. However, CBMs rely on a ‘complete’ concept set, requiring practitioners to define and label enough concepts […]
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1 event,
MSR Thesis Defense
Mohan Kumar Srirama
Learning to Generalize via Human Manipulation Priors
Abstract: Generalization is a core challenge in robotics, where the goal is to enable robots to handle novel objects, environments, and embodiments with minimal additional data. This thesis explores how human prior knowledge, captured through both passive observation and active demonstration, can be leveraged to improve generalization in manipulation tasks. We propose two complementary approaches that scale robot learning leveraging large-scale human-derived data. First, we introduce HRP (Human Affordances for Robotic Pre-Training), where we learn actionable visual representations by extracting hand trajectories, contact points, and object labels from internet-scale human videos. These representations, when used to initialize control policies, lead to significant performance gains in downstream robot manipulation tasks and transfer effectively across viewpoints and robot morphologies. Second, we present DexWild (Dexterous Human Interactions for In-the-Wild Robot Policies), a system that collects high-fidelity in-the-wild demonstrations using a human motion-capture device. A human-robot co-training algorithm combines this diverse human data with limited robot data, enabling robust policy transfer to unseen scenes, robot arms, and hands. […]
1 event,
PhD Thesis Proposal
Rohan Choudhury
Accelerating Video Understanding and Generation at Scale
Abstract: While image understanding, generation, and manipulation have matured rapidly in recent years, video remains challenging due to the significantly larger input size. As a result, tasks such as generating long videos or understanding extended video sequences remain out of reach for current models due to their computational cost. This talk presents a series of […]
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1 event,
MSR Thesis Defense
Aryan
Parameter-Efficient Neuro-Symbolic Action Anticipation via Iterative Context Refinement
Abstract: As robots and intelligent systems increasingly interact with humans, the ability to understand users by anticipating their actions becomes significantly more important. Current approaches to action anticipation leverage the inference capabilities of large foundational models but are limited in their application by the complexity and resource requirement, as well as the difficulty of training. […]
1 event,
MSR Thesis Defense
Yiran Tao
Incrementally Learned Shared Autonomy and Intelligent Mode Switching for Assistive Teleoperation
Abstract: Assistive robotic systems have the potential to significantly enhance autonomy and independence for individuals with physical disabilities. However, existing shared autonomy frameworks typically rely on static policies trained offline, which fail to adapt effectively when encountering unforeseen environmental variations or evolving user behaviors. Additionally, teleoperating high degrees-of-freedom (DoF) robotic manipulators through low-dimensional (low-DoF) user […]