Collaborative manipulation requires robots to perform complementary actions as interactions unfold. We study single-policy decentralized collaboration: every robot runs the same policy from its visual observations and proprioception, without task prompts, identity labels, or inter-robot messages. The challenge is to le...
Ya-Nan Zhou, Zhao-Yan Qian, Zi-Hao Li et al.· 0 citations
Learning from human videos offers a promising route to acquiring diverse manipulation skills. Extending this capability beyond tabletop settings to long-horizon mobile manipulation requires adapting and composing demonstrated interactions across changing scenes and robot configurations. We present Skill Assembly and Ki...
StereoPatch is introduced, a patch-aligned RGB-depth representation that binds registered metric geometry directly to the RGB patches used for action prediction and suggests that resolving control-relevant geometric ambiguity benefits from aligning depth directly with the visual features used for action prediction.
Ya-Nan Zhou, Zhao-Yan Qian, James Zhao et al.· 0 citations
Successful mobile manipulation requires coordinated base and arm motion while maintaining accurate spatial positioning. However, demonstration-trained policies can struggle to realise the intended base motion reliably, leading to spatial misalignment and subsequent manipulation failures. We present MAVP (Map-Aware Visu...
POISE (Palm-relative Object reaching In SE(3), a sim-to-real reinforcement learning framework for in-hand 6D object pose reaching, combines diverse stable-grasp initialization, goal- and geometry-conditioned control, an adaptive 6D goal curriculum, and a compact reward scheme for pose reaching and grasp preservation.
Jun-Xiao Lin, Tian-Yue Wu, Jie Yin et al.· 0 citations
NestDex, a nested policy-learning framework that reduces this burden by using learned hand skills to assist demonstration collection, improves demonstration reliability and efficiency, and the resulting empirical evaluations support effective autonomous policy learning.
James Zhao, Jin-He Tang, Ming-Yuan Ba et al.· 1 citation
AutoIntervene is presented, an online framework that selectively transfers control between an action-chunking policy and an operator during deployment, and calibrate separate switching thresholds for the two directions from empirical quantiles of evaluation-level scores on held-out expert demonstrations, avoiding direc...
This work introduces TRAjectory-routed Causal Evidence (TRACE), a memory framework for visuomotor imitation policies that stores task-relevant visual and robot-state evidence in a fixed-size latent memory that remains bounded over long episodes.
TriManPolicy is presented, a tri-manual imitation learning system that allows one operator to demonstrate behaviours for three arms while reconsidering when they occur, and policies trained on demonstrations retimed by DATS exhibit more efficient coordination while maintaining comparable observed task success.
James Zhao, Mingyuan Ba, Weiming Zhi· arXiv.org· 1 citation
This work proposes a framework known as Video-Generation Environment Representation (VGER), which leverages the advances of large-scale video generation models to generate a moving camera video conditioned on the input image, and demonstrates its ability to produce smooth motions that account for the captured geometry...
Weiming Zhi, Ziyong Ma, Tianyi Zhang et al.· Neural Information Processin...· 0 citations
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