arXiv Machine Learning June 10, 2026 By Beomjun Kim, Seong Hyeon Park, Seunghoon Sim, Seungjun Moon, Sanghyeok Lee, Jinwoo Shin

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

Read the original on arXiv Machine Learning →

arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning

Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration

LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition

EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets

Pre-training Visual Dexterity in Simulation

Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos