Guocai Yang — 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 ↗

2025

Synergistic Terrain-Adaptive Morphing and Trajectory Tracking in a Transformable-Wheeled Robot

Ke Shi, Zainan Jiang, Borui Liu, Guocai Yang, Minghe Jin

RA-L 2025

Transformable-wheeled robots exhibit efficient locomotion and obstacle negotiation through mode transformation, which underpins the development of the multimodal robot MTABot—a previously validated platform. However, existing literature primarily focuses on structural design, leaving autonomous mode

Cited by 3 Source ↗Scholar ↗