General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To investigate this, we introduce RobotWorld, a challenging simulation testbed for robot use: turning instructions and observations into physic...
Zhiqin Yang, Chen-Xin Li, Xiao-Meng Hu et al.· 2 citations
Robot foundation policies predict action chunks, but how many actions to execute before replanning depends on the current task phase. We introduce ChunkTrust, which treats the execution horizon as a latent variable inferred from action-expert evidence rather than a fixed hyperparameter. Its training-free Action-aware H...
Fan-Ding Huang, Jing-Yan Jiang, Shi-Feng Bao et al.· 0 citations
RoboHarn-Evo is introduced, a dual-loop harness that evolves Hierarchical Physical Knowledge (HPK) from physical experience that demonstrates that physical interaction can be accumulated into reusable knowledge for improving subsequent manipulation.
Shi-Feng Bao, Fan-Ding Huang, Yi-Han Lin et al.· 3 citations
Humans perform long-horizon manipulation by retaining knowledge of what earlier actions have established while continuously adapting the motion underway. By contrast, action-chunked vision-language-action (VLA) policies repeatedly replan from the current input at each query. Existing methods preserve either long-term t...
Yuzhi Huang, Wei Bu, Ziyi Xiong et al.· 1 citation
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