Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility introduces two central challenges: spatially grounded perception under continuous ego-motion and coordinated control of heterogeneous arm and base actions. Existing appro...
Qi-Wei Liang, Guang-Yu Chen, Shao-Long Zhu et al.· 0 citations
MoPA is presented, a framework that aligns perceptual conditioning with mobility and manipulation while preserving coordination at the action level, and achieves state-of-the-art performance across all three task suites.
Guang-Yu Chen, Qi-Wei Liang, Shao-Long Zhu et al.· 2 citations
GeoLAM, a framework for learning geometry-grounded latent actions from action-free human videos, combines future-frame reconstruction through a frozen geometric feature hierarchy with motion supervision from a training-only 4D geometry teacher and requires neither the geometry teacher nor future-video generation.
Yi-Fan Xie, He-Kun Tian, Jin-Kun Liu et al.· 0 citations
Vision-language-action (VLA) policies commonly execute long-horizon mobile manipulation through open-loop action chunks, issuing multiple actions without receiving new high-level visual input. A committed chunk therefore implies how observations should evolve, but accidental deviations can violate this expectation whil...
Xiao-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency and across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods.
Xiaomin Guo, Piao-Piao Jin, Jason Li et al.· arXiv.org· 16 citations· ⚡2
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