Zichun Xu — accepted AI-conference papers

2026

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks

Zichun Xu, Zhaomin Wang, Yuntao Li, Lei Zhuang, Zhiyuan Zhao, Guocai Yang +1

RA-L 2026

For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This paper draws insights from this behavior to enable agents to learn efficient assembly strategies through visual reinforcement learning. Hence, we propose a

Cited by 0 Source ↗Scholar ↗

2024

Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making

Lei Zhuang, Jingdong Zhao, Yuntao Li, Zichun Xu, Liangliang Zhao, Hong Liu

RA-L 2024

Sampling-based motion planning (SBMP) algorithms are renowned for their robust global search capabilities. However, the inherent randomness in their sampling mechanisms often results in inconsistent path quality and limited search efficiency. In response to these challenges, this work proposes a nov

Cited by 5 Source ↗Scholar ↗