General-purpose robot agents must learn from experience, transfer to new tasks, and act efficiently. Code as Policies (CaP) methods generate and repair programs at runtime, incurring latency and entangling reusable mechanisms with task-specific decisions. We introduce RACaP, an agentic framework that moves coding to ev...
Ze-Xi Li, Ye-Hang Zhang, Hao-Jian Huang et al.· 0 citations
Part-level 3D assets are essential for editing, reassembly, and interaction, yet recovering such structure from a single image remains challenging due to occlusion, ambiguous boundaries, and the need for coherent multi-part reasoning. Existing approaches struggle to achieve both controllable part-level generation and c...
Jian-Tao Lin, Mei-Xi Chen, Ying-Jie Xu et al.· 0 citations
General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this process by using demonstrations and interaction to direct existing competence with neural parameters held fixed during deployment. We organize...
Hao-Jian Huang, Ze-Xi Li, Ju-Hao Guo et al.· 0 citations
Robo-Harness K1 is introduced, a robot-use agent (RUA) framework that exposes perception as tools that makes 3D geometry accessible without changing the VLM architecture or training a depth encoder, and suggests that perception-augmented RUAs offer a promising route to sample-efficient, generalizable robotic policies t...
Ze-Xi Li, Ye-Hang Zhang, Wen-Qian Li et al.· 0 citations
World Action Agent is presented, a multi-agent harness through which VLMs pilot robots with basic tools, making every decision within a visual action workspace, outperforming end-to-end VLAs, code-as-policy agents, and a visual-harness baseline with the same backbone.
Ye-Hang Zhang, Hao-Jian Huang, Yi-Fan Chang et al.· 0 citations
A cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios, and two official Codabench tracks.
Yu-Qian Fu, Tianwen Qian, Yanjun Li et al.· 0 citations
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