A reinforcement learning formulation is presented to solve in-hand assembly: mating two rigid objects within a single dexterous hand, with no second arm and no fixture, which shows robustness to state-estimation errors caused by oc- clusion.
Liu-Ao Pei, Tian-Yue Wu, Hui Zhang et al.· 1 citation
This work presents a unified framework that models all four skills in a single formulation that shares the same state and action spaces and a common objective structure, and suggests that different dexterous manipulation skills can be viewed as instantiations of a shared task formulation, revealing the intrinsic consis...
Hui Zhang, Julian Ferchow, Jie Song et al.· arXiv.org· 3 citations
This paper introduces a new Kinematic Normalizing Flow (KNF) model, trained on a large-scale kinematic dataset, that generates diverse yet feasible partial reference motions that effectively addresses whole-body control challenges by decomposing the complex loco-manipulation problem into partial reference motion genera...
Zhengmao He, Moonkyu Jung, Hyeongjun Kim et al.· arXiv.org· 0 citations
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