9 papers
Scaling Cross-Embodiment World Models for Dexterous Manipulation
Zihao He, Bo Ai, Tongzhou Mu +6
Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data shar…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control
Zhuoqun Chen, Xiu Yuan, Tongzhou Mu +1
Imitation learning is an efficient method for teaching robots a variety of tasks. Diffusion Policy, which uses a conditional denoising diffusion process to generate actions, has de…
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
Adrià López Escoriza, Nicklas Hansen, Stone Tao +2
Long-horizon tasks in robotic manipulation present significant challenges in reinforcement learning (RL) due to the difficulty of designing dense reward functions and effectively e…
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…
When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?
Tongzhou Mu, Zhaoyang Li, StanisÅaw Wiktor Strzelecki +4
Learning policies from high-dimensional visual inputs, such as pixels and point clouds, is crucial in various applications. Visual reinforcement learning is a promising approach th…