96 citations · 456 across the 88 of their papers we have counts for
95 papers
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…
Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery
Akhil Bagaria, Anita De Mello Koch, George Konidaris
Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target all aspects of the state sim…
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…
From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Saket Tiwari, Tejas Kotwal, George Konidaris
We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on…
From Noise to Control: Parameterized Diffusion Policies
Renhao Zhang, Haotian Fu, Mingxi Jia +3
We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior ma…
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…
JAXenstein: Accelerated Benchmarking for First-Person Environments
Ruo Yu Tao, George Konidaris
The progression of reinforcement learning algorithms have been driven by challenging benchmarks. The rate in which a researcher can iterate on a problem setting directly impacts th…
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…
Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion
Dan Haramati, Carl Qi, Tal Daniel +3
We propose a hierarchical entity-centric framework for offline Goal-Conditioned Reinforcement Learning (GCRL) that combines subgoal decomposition with factored structure to solve l…
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…
Learning Parameterized Skills from Demonstrations
Vedant Gupta, Haotian Fu, Calvin Luo +2
We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy…
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…
From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
Rafael Rodriguez-Sanchez, Cameron Allen, George Konidaris
Algorithms that exploit factored Markov decision processes are far more sample-efficient than factor-agnostic methods, yet they assume a factored representation is known a priori -…
Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains
Ruo Yu Tao, Kaicheng Guo, Cameron Allen +1
Mitigating partial observability is a necessary but challenging task for general reinforcement learning algorithms. To improve an algorithm's ability to mitigate partial observabil…
Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces
Saket Tiwari, Omer Gottesman, George Konidaris
Advances in reinforcement learning (RL) have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most th…
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…