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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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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260701T143000
DTEND;TZID=America/New_York:20260701T153000
DTSTAMP:20260707T165808
CREATED:20260625T193130Z
LAST-MODIFIED:20260625T193130Z
UID:151664-1782916200-1782919800@www.ri.cmu.edu
SUMMARY:Learning From History: Test-Time Verification and Adaptation for Robotics
DESCRIPTION:Abstract: The physical properties and dynamics that decide how an object or environment responds to a robot’s actions are often impossible to determine from visual observation alone. An object’s mass distribution and friction\, the kinematics of an articulated object: these latent factors dictate the correct action\, yet they leave little or no trace in a single image. This partial observability makes a purely feed-forward visual policy fundamentally limited\, and even a strong policy will inevitably make mistakes when deployed\, whether from this latent ambiguity or imperfect perception. \n\nThis thesis argues that both the missing information and the means to recover from error are supplied by the robot’s own history of interaction: by acting\, observing the outcome\, and reasoning about the mismatch between what was expected and what occurred\, an agent can infer the underlying dynamics online and adapt on the fly. \nWe develop this idea along two complementary axes. Through verification\, we introduce HAVE\, a History-Aware VErifier that scores action proposals from a generative policy by reasoning about past actions and their outcomes. We prove that any better-than-chance verifier improves expected reward over the generator alone\, and validate it across articulated objects\, multi-modal doors\, and uneven-mass objects in simulation and the real world. Through test-time training\, we introduce SCOUT\, a dynamics-aware meta-learning framework that couples a policy with a forward dynamics model through a shared belief latent; at deployment an inner loop revises this belief by minimizing the error between expected and observed outcomes\, while a meta-learned outer loop ensures the belief steers the policy correctly. SCOUT adapts substantially faster than history-conditioned baselines and directly sim-to-real transfers to real-world tasks. \nTogether\, these methods show that treating interaction history as a rich\, structured supervision signal\, rather than a sparse scalar reward\, yields manipulation policies that adapt quickly and robustly to the hidden physical properties and dynamics of objects and environments\, recovering from their own mistakes along the way. \nCommittee:\nProf. David Held (advisor)\nProf. Andrea Bajcsy\nMihir Prabhudesai
URL:https://www.ri.cmu.edu/event/learning-from-history-test-time-verification-and-adaptation-for-robotics/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260706T100000
DTEND;TZID=America/New_York:20260706T110000
DTSTAMP:20260707T165808
CREATED:20260630T134012Z
LAST-MODIFIED:20260630T134012Z
UID:151684-1783332000-1783335600@www.ri.cmu.edu
SUMMARY:View Generalizable Manipulation Policies via Sim-to-Real Transfer
DESCRIPTION:Abstract: Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. A key requirement for these manipulation policies is to exhibit robust generalization capabilities when deployed in the real world\, where the objects\, scenes\, and sensors a robot encounters differ from those seen during training. In practice\, learned policies often remain brittle to these changes\, which limits their usefulness beyond the narrow conditions in which they were trained.\nIn this thesis\, we study manipulation policies that remain performant under camera viewpoint shifts\, so that a single policy can be deployed across various camera poses in the real world. We approach this by grounding the policy in the robot frame in order to reason about the scene and the robot’s actions in a shared frame rather than relative to a particular camera. In the first part\, we present ArticuBot\, in which a single learned policy enables a robotics system to open diverse categories of unseen articulated objects in the real world. The policy operates on point clouds in the robot frame\, and we find it remains robust under camera viewpoint changes\, including camera poses not seen during training\, while generalizing across objects that vary widely in geometry\, size\, and articulation. By generating a large number of demonstrations in physics-based simulation and distilling the demonstrations into a hierarchical\, point cloud-based neural policy via imitation learning\, we demonstrate an effective policy learning approach that also achieves object-level generalization. \nIn the second part\, we bring this robot-frame reasoning to image-based policies\, which benefit from large-scale pretraining and scalability that point cloud policies do not. We present VGP\, an image-based policy that encodes the scene with geometry-aware visual features and grounds its visual\, proprioception\, and action tokens in a shared robot frame\, allowing it to remain robust across a wide range of camera poses\, outperforming 2D and 3D baselines\, while matching fixed-camera baselines. As a practical consequence\, our policy transfers zero-shot from simulation to the real world under random camera configurations. \nAcross these two parts\, we show how large-scale simulation and imitation learning\, together with grounding the policy in the robot frame\, can be used to train manipulation policies that remain robust as the camera viewpoint changes and transfer to the real world. \n\nCommittee: \nProf. David Held (co-chair)\nProf. Zackory Erickson (co-chair)\nProf. Shubham Tulsiani\nKrishna Suresh
URL:https://www.ri.cmu.edu/event/view-generalizable-manipulation-policies-via-sim-to-real-transfer/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260708T150000
DTEND;TZID=America/New_York:20260708T160000
DTSTAMP:20260707T165808
CREATED:20260706T145238Z
LAST-MODIFIED:20260706T145238Z
UID:151713-1783522800-1783526400@www.ri.cmu.edu
SUMMARY:Learning-Guided Search over Continuous Actions for Long-Horizon Robot Manipulation
DESCRIPTION:Abstract:\nDespite recent advances in policy learning\, long-horizon manipulation remains difficult because learned policies must avoid compounding errors while preserving future feasibility. While search-based planning can explicitly reason over future consequences\, it becomes expensive in high-dimensional continuous action spaces. Classical Task and Motion Planning methods address this by introducing symbolic objects\, relations\, and abstractions for discretization\, but these representations are difficult to define reliably from partial real-world observations. This thesis aims to address this gap by asking: How can learning and search be integrated directly over continuous actions? We study this question in multi-object rearrangement tasks\, such as packing a constrained shelf or organizing dinnerware on a table\, where the robot must execute multiple pick-and-place actions while preserving downstream task feasibility.\n\nWe first introduce SPOT (Search over Point cloud Object Transformations)\, a system that combines search with learning at test time. SPOT formulates object rearrangement as A* search over object-wise SE(3) transformations from partially observed point clouds. Learned suggesters guide which object to move and where to move it\, while a learned model-deviation estimator biases search toward executable transitions. We then introduce DISCO (Distilled Tree Search over Continuous actions)\, a system that combines search with learning at train time. DISCO uses progressive-widening Monte Carlo Tree Search (MCTS) with a learned policy and value function in GPU-parallelized simulation. MCTS-generated plans are distilled back into the policy-value model\, enabling direct execution without search at test time\, and improving action proposals in subsequent searches. Finally\, this thesis shows that learning and search can be combined in complementary ways: learned models make continuous-action search possible at test time (SPOT)\, while search at train time can extend learned models to longer-horizon tasks (DISCO).\n\nThesis Committee:\nProf. David Held (co-advisor)\nProf. Maxim Likhachev (co-advisor)\nProf. Katerina Fragkiadaki\nItamar Mishani
URL:https://www.ri.cmu.edu/event/learning-guided-search-over-continuous-actions-for-long-horizon-robot-manipulation/
LOCATION:Gates Hillman Center 4405
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260709T130000
DTEND;TZID=America/New_York:20260709T140000
DTSTAMP:20260707T165808
CREATED:20260706T145421Z
LAST-MODIFIED:20260706T145421Z
UID:151715-1783602000-1783605600@www.ri.cmu.edu
SUMMARY:Exploring High-Level Goal Prediction for Hierarchical Imitation Learning in Robotic Manipulation
DESCRIPTION:Abstract:\nHierarchical imitation learning has become an effective approach for robotic manipulation: a high-level policy predicts a sub-goal end-effector pose\, while a low-level policy executes the actions needed to reach it. This decomposition improves generalization and provides an interpretable interface\, but the design of the high-level goal predictor remains an open question. \n\nThis thesis studies high-level goal prediction through three investigations. First\, on the MimicGen benchmark\, we compare dense predicted goal point clouds with a sparse four-point end-effector representation\, finding that the sparse representation provides a more reliable conditioning signal across tasks. Second\, on RLBench\, we extend the four-point predictor with language\, RGB features\, gripper actions\, and collision-ignore decisions\, pairing it with motion planning to achieve competitive performance with state-of-the-art keyframe-action prediction methods. Third\, in a sim-to-real setting\, we make the high-level predictor steerable through prompting\, allowing users to select among multiple valid targets\, such as which drawer to open. \n\nTogether\, these studies clarify how representation\, capability\, and controllability shape high-level goal prediction for hierarchical imitation learning in robotic manipulation. \n\nThesis Committee:\nProf. David Held (co-advisor)\nProf. Zackory Erickson (co-advisor)\nProf. Shubham Tulsiani\nYilin Wu
URL:https://www.ri.cmu.edu/event/exploring-high-level-goal-prediction-for-hierarchical-imitation-learning-in-robotic-manipulation/
LOCATION:Gates 6115
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260709T140000
DTEND;TZID=America/New_York:20260709T150000
DTSTAMP:20260707T165808
CREATED:20260706T191935Z
LAST-MODIFIED:20260706T191935Z
UID:151734-1783605600-1783609200@www.ri.cmu.edu
SUMMARY:Beyond Vision-Language-Action Models: Adapting\, Steering\, and Accelerating Generalist Robot Policies
DESCRIPTION:Abstract: Generalist robot policies\, vision-language-action models that combine a large pretrained vision-language model backbone with a diffusion or flow-matching action head\, are increasingly capable\, yet hard to deploy in the real world. Three gaps separate such a policy from a deployable one: a data gap (adapting to a new task still demands task-specific teleoperation data)\, an inference gap (the policy samples its action distribution with no control over how conservative or diverse the result is)\, and an architecture gap (a large policy is too slow to replan often\, so it must run its action chunks open-loop and cannot react mid-motion). This thesis argues that these gaps can be closed not by training larger models on more robot data\, but by changing how a pretrained policy generates its actions\, with little or no additional training. \nWe develop three methods. DemoDiffusion (data) imitates a single human demonstration instead of collecting teleoperation data: it retargets the human hand motion into a coarse robot trajectory\, then uses a frozen generalist diffusion policy to refine it into plausible robot actions. It needs no task-specific or paired human-robot data\, and succeeds even where the base policy fails outright. Temporal Score Rescaling (inference) rescales the learned score/flow at inference to draw from a sharper or broader distribution than the model was trained on. It is training-free\, works with any diffusion or flow model\, and improves image generation\, depth\, pose\, and protein design alongside robot policies. πR² (architecture\, inference) builds on diffusion forcing to split conditioning into a fast proprioceptive channel and a slow\, asynchronously updated vision-language channel\, so the policy reacts to fresh proprioception while tolerating stale vision. A latency-adaptive schedule lets a single model handle varying inference latency and emit actions in a single denoising step\, making a large policy reactive and real-time\, several times faster than the original. \nTogether\, these methods adapt\, steer\, and accelerate a pretrained policy\, taking vision-language-action models beyond what they can do as trained and toward real-world deployment. \nCommittee: \nProf. Shubham Tulsiani (advisor)\nProf. Katerina Fragkiadaki\nAndrew Wang
URL:https://www.ri.cmu.edu/event/beyond-vision-language-action-models-adapting-steering-and-accelerating-generalist-robot-policies/
LOCATION:GHC 9115
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260710T110000
DTEND;TZID=America/New_York:20260710T120000
DTSTAMP:20260707T165808
CREATED:20260706T185409Z
LAST-MODIFIED:20260706T185409Z
UID:151732-1783681200-1783684800@www.ri.cmu.edu
SUMMARY:Towards Scalable Robot Learning: From Teleoperation to Web-scale Data
DESCRIPTION:Abstract:\nHumanoid robots operating in human environments must manipulate articulated objects under contact and kinematic constraints that human demonstrations do not satisfy. That mismatch makes the human–humanoid embodiment gap the central bottleneck for learning from human data: robot demonstrations are expensive and sparse\, while human demonstrations inhabit a different state-action space and often violate robot kinematic constraints. This thesis studies how to convert human behavior into supervision that remains executable for the target robot body. \nThe first part develops Humanoid Policy ~ Human Policy for cross-embodiment supervision in humanoid manipulation. It places humans and humanoids in a unified state-action representation\, enabling a transformer policy to co-train on human and robot demonstrations and retarget its predictions at deployment. To support this formulation\, we introduce PhD^2\, a task-oriented egocentric human demonstration dataset that expands data scale without discarding embodiment structure.\nThe second part presents EmbodyHOI\, which addresses a harder embodiment-gap setting in dexterous hand-object interaction. It starts from a flow-matching diffusion model trained in human hand-object space\, then applies a differentiable guidance function during sampling to steer trajectories toward a target humanoid embodiment\, jointly optimizing wrist reachability and base placement before downstream control. \nTogether\, these chapters show that scalable robot manipulation requires data transformations that preserve task structure while respecting the robot body. \n\n\nThesis Committee:\nProf. Guanya Shi (advisor)\nProf. Laszlo A. Jeni\nEliot Xing
URL:https://www.ri.cmu.edu/event/towards-scalable-robot-learning-from-teleoperation-to-web-scale-data/
LOCATION:Gates Hillman Center 4405
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260713T140000
DTEND;TZID=America/New_York:20260713T153000
DTSTAMP:20260707T165808
CREATED:20260706T144344Z
LAST-MODIFIED:20260706T144344Z
UID:151711-1783951200-1783956600@www.ri.cmu.edu
SUMMARY:Consistent Modeling of 4D Scenes for Perception and Generation
DESCRIPTION:Abstract:\nA core challenge in vision is building representations that capture 3D scenes over time for both perception and generation. This thesis studies consistency across views\, time\, and modalities by moving from dense grid-based representations toward entity-centric scene representations that can be maintained across frames and used for interactive generation. \nThe first part of the thesis develops consistent 3D perception systems\, including methods for temporal multi-camera 3D detection\, depth completion\, semi-supervised detection\, and streaming semantic occupancy estimation. These works progressively move from dense spatial representations toward persistent\, query-based representations that model both foreground objects and background structure over time. \nThe second part of the thesis presents LatentWorld\, a generative model for entity-centric 4D scene generation. LatentWorld represents a scene as a sparse set of grounded 3D latents\, assigning persistent latents to foreground actors while using multiple latents for background regions. Generation is factorized into layout\, geometry\, and motion\, enabling temporally coherent semantic-occupancy rollouts with stable actor identity\, explicit ego and actor motion\, and direct scene-level control. \nTogether\, these works show how consistent\, entity-centric representations can serve as a common foundation for understanding dynamic 3D scenes and generating plausible\, controllable 4D worlds. \nThesis Committee Members:\nKris Kitani (Chair)\nDeva Ramanan\nShubham Tulsiani\nWei-Chiu Ma (Cornell University)
URL:https://www.ri.cmu.edu/event/consistent-modeling-of-4d-scenes-for-perception-and-generation/
LOCATION:GHC 4405
CATEGORIES:PhD Thesis Defense,Student Talks
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