RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models

no code implementations • 11 Mar 2024 • Liangliang Chen, Yutian Lei, Shiyu Jin, Ying Zhang, Liangjun Zhang

In this paper, we propose RLingua, a framework that can leverage the internal knowledge of large language models (LLMs) to reduce the sample complexity of RL in robotic manipulations.

Prompt Engineering Reinforcement Learning (RL)

GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning

no code implementations • 9 Mar 2023 • Yaru Niu, Shiyu Jin, Zeqing Zhang, Jiacheng Zhu, Ding Zhao, Liangjun Zhang

In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.

Position reinforcement-learning +1

Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties

no code implementations • 29 Jan 2021 • Shiyu Jin, Xinghao Zhu, Changhao Wang, Masayoshi Tomizuka

Peg-in-hole assembly is a challenging contact-rich manipulation task.

Robotics