@inproceedings{luo2026mvr,title={MVR: Multi-view Video Reward Shaping for Reinforcement Learning},author={Luo, Lirui and Zhang, Guoxi and Xu, Hongming and Yang, Yaodong and Fang, Cong and Li, Qing},booktitle={The Fourteenth International Conference on Learning Representations},location={Rio de Janeiro, Brazil},year={2026},url={https://openreview.net/forum?id=7lw6s9ELfr},}
SYNERGAI: Perception Alignment for Human-Robot Collaboration
@inproceedings{chen2025synergai,title={SYNERGAI: Perception Alignment for Human-Robot Collaboration},author={Chen, Yixin and Zhang, Guoxi and Zhang, Yaowei and Xu, Hongming and Zhi, Peiyuan and Li, Qing and Huang, Siyuan},booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},year={2025},}
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations Spotlight (top 3.5%)
Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, and Qing Li
In Proceedings of the Forty-First International Conference on Machine Learning, 2024
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies. NS-RL entails structured state representations for tasks with visual observations, but previous methods cannot refine the structured states with rewards due to a lack of efficiency. Accessibility also remains an issue, as extensive domain knowledge is required to interpret symbolic policies. In this paper, we present a neuro-symbolic framework for jointly learning structured states and symbolic policies, whose key idea is to distill the vision foundation model into an efficient perception module and refine it during policy learning. Moreover, we design a pipeline to prompt GPT-4 to generate textual explanations for the learned policies and decisions, significantly reducing users’ cognitive load to understand the symbolic policies. We verify the efficacy of our approach on nine Atari tasks and present GPT-generated explanations for policies and decisions.
@inproceedings{pmlr-v235-luo24j,title={End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations},author={Luo, Lirui and Zhang, Guoxi and Xu, Hongming and Yang, Yaodong and Fang, Cong and Li, Qing},booktitle={Proceedings of the Forty-First International Conference on Machine Learning},pages={33533--33557},year={2024},publisher={PMLR},location={Vienna, Austria},badge={Spotlight (top 3.5%)}}
