{"ID":6023601,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T14:11:27.630055639Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06323","arxiv_id":"2607.06323","title":"LAMP: Latent Motion Prior-Guided Real-World Learning for Dexterous Hand Manipulation","abstract":"Real-world learning for dexterous hands remains brittle because high-dimensional hand actions amplify imitation errors and make reinforcement-learning exploration prone to contact-breaking motion. While combining imitation learning (IL) with online reinforcement learning (RL) can reduce manual supervision, unconstrained exploration in raw hand-action spaces is sample-inefficient and risky for physical hardware. We introduce a latent motion prior module (\\prior{}) that maps recent hand-action histories to a compact, history-conditioned latent prior and decodes continuous latent commands into executable high-dimensional hand targets. Built on this prior, \\method{} is a three-stage real-world dexterous learning framework: it pretrains \\prior{} from demonstrations, trains a visuomotor policy that predicts native arm commands and latent hand-action offsets, and improves the policy with online residual RL in the same latent hand-action space. This shared, decodable interface lets residual exploration make local corrections near demonstrated, contact-consistent hand motions rather than perturbing every finger joint independently. We evaluate \\method{} on four real-robot dexterous manipulation tasks against raw, linear, and discrete hand-action interfaces. Starting from small task-specific demonstration sets, \\method{} achieves a 56.25\\% average IL success rate and raises it to 98.75\\% after online RL, reaching 100\\% final success on three tasks and 95\\% on the remaining task.","short_abstract":"Real-world learning for dexterous hands remains brittle because high-dimensional hand actions amplify imitation errors and make reinforcement-learning exploration prone to contact-breaking motion. While combining imitation learning (IL) with online reinforcement learning (RL) can reduce manual supervision, unconstraine...","url_abs":"https://arxiv.org/abs/2607.06323","url_pdf":"https://arxiv.org/pdf/2607.06323v1","authors":"[\"Xinye Yang\",\"Zhiyuan Ma\",\"Hongze Yu\",\"Yuanpei Chen\",\"Yaodong Yang\",\"Xiaojie Chai\",\"Xinlei Chen\",\"Chao Yu\"]","published":"2026-07-07T14:22:31Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false}