Bingyi Mao — accepted AI-conference papers

2025

MambaSlip: A Novel Multimodal Large Language Model for Real-Time Robotic Slip Detection

Shaohua Zhang, Haoze Li, Bingyi Mao, Fengda Zhao, Wenbai Chen, Guowei Gao +1

RA-L 2025

The current robotic sliding detection tasks lack an effective contextual reasoning mechanism, which leads to inaccurate decision-making in unknown environments. To address this issue, we propose MambaSlip, which leverages the advantages of large language models (LLMs) in context understanding and re

Cited by 2 Source ↗Scholar ↗

2024

MARRGM: Learning Framework for Multi-Agent Reinforcement Learning via Reinforcement Recommendation and Group Modification

Peiliang Wu, Liqiang Tian, Qian Zhang, Bingyi Mao, Wenbai Chen

RA-L 2024

Sample usage efficiency is an important factor affecting the convergence speed of multi-agent deep reinforcement learning (MADRL) algorithms. Most existing experience replay (ER) methods manually select experience samples to update the agent's policy. It is difficult to give suitable and efficient e

Cited by 6 Source ↗Scholar ↗