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
How should a robot learn to manipulate objects so fragile that sub-Newton contact forces can cause irreversible damage? Existing visuo-tactile policy learning typically treats tactile sensing as an additional policy input. In direct-contact force-sensitive manipulation, however, the bottleneck can arise earlier, during...
Zi-Yang Feng, Zi-Zhao Yuan, Yu-Long Fu et al.· 0 citations
AGC-VLN (Air-Ground Collaborative VLN), the first training-free baseline for air-ground collaborative VLN, establishes that training-free methods decompose navigation into VLM-based semantic reasoning and deterministic geometric execution, exposing a collaboration interface.
Shu-Ning Zhang, Liang Li, Yun-Heng Wang et al.· 1 citation· ⚡1
DreamTrajectory is presented, a trajectory-guided framework for language-conditioned mobile manipulation that introduces one component for each limitation of existing Vision-Language-Action policies, and jointly predicts an intention-level end-effector trajectory and a whole-body action chunk in a single action expert.
Zheng Yang, Wen-Jie Zhang, Xiang-Yu Chen et al.· 1 citation
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