96 citations · 456 across the 88 of their papers we have counts for
37 papers · 1 filter
DefFlow: Goal-Conditioned Deformable Object Manipulation using 3D Point Flows
Gaurav Singh, Sergio Orozco, Ryan Duong +3
Manipulating deformable objects toward desired 3D configurations is a fundamental capability for robots operating in unstructured physical environments. However, as the range of po…
Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning
Everest Yang, Skye Thompson, George D. Konidaris
Adapting to changes in robot dynamics requires learning from new data without discarding experience that may still be useful. In continual model-based reinforcement learning (RL),…
Hydra-0: Action Flow for Generalist World Modeling and Control
Hongyu Li, Bowen Wen, Xinghao Zhu +8
We introduce Hydra-0, a generalist world model conditioned on action flow, which represents robot actions as pixel motion. This shared visual interface enables generalist world mod…
Deliberate Practice: Learning Robot Skills under a Budget
Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut +2
We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks. We propose an active skill learning algorithm, \emph{Deliberate…
Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability
Everest Yang, Skye Thompson, George D. Konidaris
Surgical tissue retraction requires effective manipulation planning under partial and noisy perception. We study state estimation for deformable tissue retraction, where only spars…
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Hongyu Li, Wanjia Fu, Xiaoyan Cong +11
Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to…
Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
Benedict Quartey, Sebastian Castro, Eric Rosen +3
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skil…
Creative Robot Tool Use by Counterfactual Reasoning
M. Tuluhan Akbulut, Varun Satheesh, Ahmed Jaafar +5
We propose a causal reasoning framework for creative robot tool use where a suitable tool for a task is correctly identified for use beyond its primary objectives. The proposed fra…
Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions
Sergio Orozco, Tushar Kusnur, Brandon May +2
Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D…
One-Shot Cross-Geometry Skill Transfer through Part Decomposition
Skye Thompson, Ondrej Biza, George Konidaris
Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to adapt to objects with unfami…
NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning
Jiahui Fu, Junyu Nan, Lingfeng Sun +7
Solving complex long-horizon robotic tasks requires joint reasoning over abstract task structure and low-level physical interaction. While combining Vision-Language Models (VLMs) a…
Effective Task Planning with Missing Objects using Learning-Informed Object Search
Raihan Islam Arnob, Max Merlin, Abhishek Paudel +3
Task planning for mobile robots often assumes full environment knowledge and so popular approaches, like planning via the PDDL, cannot plan when the locations of task-critical obje…
SkillWrapper: Generative Predicate Invention for Task-level Robot Planning
Ziyi Yang, Benned Hedegaard, Ahmed Jaafar +8
Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic repres…
NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos
Hongyu Li, Lingfeng Sun, Yafei Hu +4
Enabling robots to execute novel manipulation tasks zero-shot is a central goal in robotics. Most existing methods assume in-distribution tasks or rely on fine-tuning with embodime…
Optimal Interactive Learning on the Job via Facility Location Planning
Shivam Vats, Michelle Zhao, Patrick Callaghan +4
Collaborative robots must continually adapt to novel tasks and user preferences without overburdening the user. While prior interactive robot learning methods aim to reduce human e…
Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills
Benned Hedegaard, Yichen Wei, Ziyi Yang +4
Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems. Although TAMP methods have historically assumed that each task-leve…
V-HOP: Visuo-Haptic 6D Object Pose Tracking
Hongyu Li, Mingxi Jia, Tuluhan Akbulut +3
Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this mul…
Skill Generalization with Verbs
Rachel Ma, Lyndon Lam, Benjamin A. Spiegel +6
It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a give…
Bootstrapping Object-level Planning with Large Language Models
David Paulius, Alejandro Agostini, Benedict Quartey +1
We introduce a new method that extracts knowledge from a large language model (LLM) to produce object-level plans, which describe high-level changes to object state, and uses them…
Verifiably Following Complex Robot Instructions with Foundation Models
Benedict Quartey, Eric Rosen, Stefanie Tellex +1
When instructing robots, users want to flexibly express constraints, refer to arbitrary landmarks, and verify robot behavior, while robots must disambiguate instructions into speci…
Improved Inference of Human Intent by Combining Plan Recognition and Language Feedback
Ifrah Idrees, Tian Yun, Naveen Sharma +4
Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating m…
Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills
Seiji Shaw, Devesh K. Jha, Arvind Raghunathan +4
Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and s…
LTL-Transfer: Skill Transfer for Temporal Task Specification
Jason Xinyu Liu, Ankit Shah, Eric Rosen +3
Deploying robots in real-world environments, such as households and manufacturing lines, requires generalization across novel task specifications without violating safety constrain…
Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
Adam Pacheck, Steven James, George Konidaris +1
We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that we use to automatically encode the robot's capabilities in Linear Te…