5 papers
Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning
Shangzhe Li, Zhiao Huang, Hao Su
Imitation Learning (IL) has achieved remarkable success across various domains, including robotics, autonomous driving, and healthcare, by enabling agents to learn complex behavior…
A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models
Yilin Wang, Shangzhe Li, Haoyi Niu +3
We are interested in solving the problem of imitation learning with a limited amount of real-world expert data. Existing offline imitation methods often struggle with poor data cov…
Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation
Tongxuan Tian, Haoyang Li, Bo Ai +3
Cloth manipulation is challenging due to its highly complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions, which complicate both state estimation and dyn…
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…
Reward-free World Models for Online Imitation Learning
Shangzhe Li, Zhiao Huang, Hao Su
Imitation learning (IL) enables agents to acquire skills directly from expert demonstrations, providing a compelling alternative to reinforcement learning. However, prior online IL…