arXiv:2606.28813v1 Announce Type: cross Abstract: Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions. However, transferring human demonstrations to robots remains challenging due to embodiment mismatch, scene variation, and robot-specific feasibility constraints. We present Human2Any, a framework for learning reusable object-centric interaction priors from human videos without requiring real-world robot demonstrations in the target task contexts. Human2Any represents manipulation through object-object in
The paper addresses a long-standing challenge in robotics of effectively transferring human knowledge to diverse robotic platforms, a critical step for broader AI application.
This development could significantly accelerate the training and deployment of robots in varied tasks by leveraging abundant human video data, reducing the need for costly and time-consuming robot-specific demonstrations.
Robot learning paradigms shift towards more accessible and scalable data sources, potentially democratizing advanced robot capabilities beyond specialized labs.
- · Robotics research institutions
- · Automation industries
- · AI developers
- · Hardware manufacturers
- · Companies relying on outdated robot training methods
- · Specialized robot demonstration services
Faster and more efficient robot deployment in manufacturing, logistics, and service sectors.
Increased demand for human video datasets and advanced human activity recognition AI.
Ethical and safety frameworks for human-learned robot behaviors becoming a prominent concern.
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Read at arXiv cs.AI