Mobile humanoid manipulation requires both reaching a usable workspace and preserving precise hand-object interactions as object poses and contact conditions change. Learning these behaviors from limited task-specific data remains challenging. To bridge this gap, we introduce Praxis, a whole-body manipulation framework...
Shu-Liang He, Rui-Yang Xu, Bo Yue et al.· 0 citations
The RoboSynChallenge competition introduces a unified benchmark to evaluate and advance the generalizability of manipulation policies across a spectrum of tasks, environments, and difficulty levels and aims to foster the development of broadly capable, data-efficient, and adaptable manipulation systems, thereby paving...
Runyi Zhao, Rui-Han Wu, Cheng-Kun Li et al.· 0 citations
This work proposes a hierarchical closed-loop agentic LLM-based framework to ensure robust multi-robot manipulation and achieves superior success rates, ensures robust adaptability ranging from single to cross workspace manipulation, and offers a generalizable approach for diverse manipulation tasks.
Yi-Xiang He, Lan Wei, Haoming Cen et al.· Robotics· 0 citations
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