A simulation of a real robot workspace must preserve task-relevant interactions, while policies developed in it must operate on observations available to the real robot. Yet scene reconstruction and policy development are often treated separately. We present Agentic Real-to-Sim-to-Real (Agentic RSR), a framework that l...
Yi-Han Li, Ya-Ting Feng, Sheng-Jiu Sun et al.· 1 citation
Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited intuitive force feedback, making contact-rich demonstration collection challenging for human operators. To address this, we present FOMI, a...
Yi-Shu Shen, Yi-Ming Luo, Rui-Lin Wang et al.· 0 citations
A robot that assists people must often act on what a person has shown rather than said: which of several identical cartons was pointed at, or which box was handled. The plan is executed from the final scene, whereas the evidence occurs earlier, possibly on objects that have since moved. We propose instance-anchored int...
Xin-Wei Xiao, Bo-Wen Yang, Wen-Jing Zhang et al.· 0 citations
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