Chenlin Ming

Chenlin Ming

Postgraduate student at Shanghai Jiao Tong University, engage in Robtics, Learning, RL, LLM.

Publications

You can also find my articles on my Google Scholar profile.

Stochastic Trajectory Optimization for Demonstration Imitation

Published in Arxiv, 2024

Humans often learn new skills by imitating the experts and gradually developing their proficiency. In this work, we introduce Stochastic Trajectory Optimization for Demonstration Imitation (STODI), a trajectory optimization framework for robots to imitate the shape of demonstration trajectories with improved dynamic performance. Consistent with the human learning process, demonstration imitation serves as an initial step, while trajectory optimization aims to enhance robot motion performance. By generating random noise and constructing proper cost functions, the STODI effectively explores and exploits generated noisy trajectories while preserving the demonstration shape characteristics. We employ three metrics to measure the similarity of trajectories in both the time and frequency domains to help with demonstration imitation. Theoretical analysis reveals relationships among these metrics, emphasizing the benefits of frequency-domain analysis for specific tasks. Experiments on a 7-DOF robotic arm in the PyBullet simulator validate the efficacy of the STODI framework, showcasing the improved optimization performance and stability compared to previous methods.

Recommended citation: Ming, C., Wang, Z., Zhang, B., Duan, X., & He, J. (2024). Stochastic Trajectory Optimization for Demonstration Imitation. arXiv preprint arXiv:2408.03131. https://arxiv.org/pdf/2408.03131

HiCRISP: A Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner

Published in Arxiv, 2023

The integration of Large Language Models (LLMs) into robotics has revolutionized human-robot interactions and autonomous task planning. However, these systems are often unable to self-correct during the task execution, which hinders their adaptability in dynamic real-world environments. To address this issue, we present a Hierarchical Closed-loop Robotic Intelligent Self-correction Planner (HiCRISP), an innovative framework that enables robots to correct errors within individual steps during the task execution. HiCRISP actively monitors and adapts the task execution process, addressing both high-level planning and low-level action errors. Extensive benchmark experiments, encompassing virtual and real-world scenarios, showcase HiCRISP’s exceptional performance, positioning it as a promising solution for robotic task planning with LLMs.

Recommended citation: Ming, C., Lin, J., Fong, P., Wang, H., Duan, X., & He, J. (2023). HiCRISP: A Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner. arXiv preprint arXiv:2309.12089. https://arxiv.org/pdf/2309.12089