no code implementations • 11 May 2024 • Zhixuan Xu, Chongkai Gao, Zixuan Liu, Gang Yang, Chenrui Tie, Haozhuo Zheng, Haoyu Zhou, Weikun Peng, Debang Wang, Tianyi Chen, Zhouliang Yu, Lin Shao
Our work introduces a comprehensive framework to develop a foundation model for general robotic manipulation that formalizes a manipulation task as contact synthesis.
no code implementations • 28 Mar 2024 • Chongkai Gao, Zhengrong Xue, Shuying Deng, Tianhai Liang, Siqi Yang, Lin Shao, Huazhe Xu
RiEMann learns a manipulation task from scratch with 5 to 10 demonstrations, generalizes to unseen SE(3) transformations and instances of target objects, resists visual interference of distracting objects, and follows the near real-time pose change of the target object.
no code implementations • 28 Jan 2022 • Chongkai Gao, Yizhou Jiang, Feng Chen
Hierarchical Imitation Learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.
no code implementations • 29 Sep 2021 • Chongkai Gao, Yizhou Jiang, Feng Chen
Hierarchical Imitation learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.
1 code implementation • 17 Jun 2021 • Chongkai Gao, Haichuan Gao, Shangqi Guo, Tianren Zhang, Feng Chen
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations.