Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes

1 code implementation • 9 May 2022 • Liang Xie, Hongxiang Yu, Kechun Xu, Tong Yang, Minhang Wang, Haojian Lu, Rong Xiong, Yue Wang

This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost.

Efficient learning of goal-oriented push-grasping synergy in clutter

1 code implementation • 9 Mar 2021 • Kechun Xu, Hongxiang Yu, Qianen Lai, Yue Wang, Rong Xiong

In this paper, a goal-conditioned hierarchical reinforcement learning formulation with high sample efficiency is proposed to learn a push-grasping policy for grasping a specific object in clutter.

Hierarchical Reinforcement Learning Robotics