27 citations · 54 across the 11 of their papers we have counts for
7 papers · 1 filter
SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
Stone Tao, Jie Xu, Hesam Rabeti +3
Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learnin…
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge
Li Kang, Heng Zhou, Xiufeng Song +41
Recent advancements in multimodal large language models and vision-languageaction models have significantly driven progress in Embodied AI. As the field transitions toward more com…
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…