5 papers
Co-Evolving Skill Generation and Policy Optimization
Zhiwei Zhang, Yudi Lin, Nikki Lijing Kuang +4
Skill-augmented reinforcement learning improves language agents by storing reusable procedural knowledge acquired from past experience. Existing methods typically use strong langua…
Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Models
Chenrui Tie, Shengxiang Sun, Yudi Lin +9
Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Conn…
Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation
Zhiwei Zhang, Xiaomin Li, Yudi Lin +8
Large Language Models (LLMs) trained with reinforcement learning and verifiable rewards have achieved strong results on complex reasoning tasks. Recent work extends this paradigm t…
VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
Chongkai Gao, Zixuan Liu, Zhenghao Chi +8
Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action gener…
Behavioral Bias of Vision-Language Models: A Behavioral Finance View
Yuhang Xiao, Yudi Lin, Ming-Chang Chiu
Large Vision-Language Models (LVLMs) evolve rapidly as Large Language Models (LLMs) was equipped with vision modules to create more human-like models. However, we should carefully…