AXIS, a growable community-driven data engine and benchmark for scalable robot learning, is presented, which enables browser-based teleoperation for large-scale demonstration collection, automatically generates and validates new manipulation tasks, and transforms community-collected demonstrations into training-ready d...
Real-world experiments demonstrate that a single $\omega$-0 model can produce smooth manipulate-while-moving behaviors and consistently outperform representative imitation learning, VLA, humanoid, and WAM baselines.
It is shown that robots can generalize effectively under dynamics uncertainties even with limited training data by leveraging a feedback mechanism, namely PhyFilter, that corrects learning outputs with physics-filtered learning residuals, and shows that physics-filtered feedback can serve as a powerful alternative to m...
Jin-Dou Jia, Shixu Han, Meng Wang et al.· npj Robotics· 2 citations
This work proposes UCA-Flow, a unified condition-action modeling framework for accurate one-step action generation that unifies observation conditions, timestep conditions, interval conditions, and action tokens into a single sequence, and processes them with a Unified Condition-Action Transformer for joint condition-a...
Xin-Yu Zhou, Zikun Cai, Kuangji Zuo et al.· 0 citations
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