27 citations · 58 across the 13 of their papers we have counts for
4 papers · 1 filter
Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model
Xiu Yuan, Tongzhou Mu, Stone Tao +3
Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often lim…
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
Arth Shukla, Stone Tao, Hao Su
High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as…
ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
Stone Tao, Fanbo Xiang, Arth Shukla +20
Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks an…
Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning
Stone Tao, Arth Shukla, Tse-kai Chan +1
Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve c…