LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
arXiv:2606.11628v1 Announce Type: cross Abstract: The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments. In contrast, unstructured human videos provide a scalable alternative. They contain diverse manipulation demonstrations across objects, scenes, and strategies, but are not directly connected to robot action. We propose LUCID, a two-stage framework that learns task intent from unstructured human videos drawn from internet-scale datasets and learns robot control in
The proliferation of internet video data combined with advances in AI models for understanding unstructured information makes this approach viable now.
This development significantly lowers the barrier to acquiring robot skills, moving away from expensive and embodiment-specific data collection towards scalable and diverse internet resources.
Robot skill acquisition shifts from a bespoke, high-cost process to one that can leverage vast, pre-existing human video datasets for intent understanding, accelerating deployment and versatility.
- · Robotics companies
- · AI model developers
- · Automation sector
- · Dexterous manipulation research
- · Human robot demonstrators
- · Developers of custom robot simulation environments
Robots will be able to learn and perform complex manipulation tasks more quickly and at a lower cost.
This acceleration in robot skill acquisition could lead to a faster integration of dexterous robots into a wider range of industries.
The ability of robots to autonomously learn from human behaviors could lead to new forms of human-robot collaboration and interaction, blurring lines between human and machine capabilities.
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Read at arXiv cs.AI