arXiv:2607.06988v1 Announce Type: cross Abstract: Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot demonstrations, task-specific fine-tuning, or long-context conditioning. We present WAM-TTT, a test-time training framework for steering world action models from raw human videos. Rather than treating human videos as trajectories to imitate, WAM-TTT absorbs them into a lightweight adaptive memory inside a frozen WAM through self-supervised video prediction. To make this memory useful for control, we
The rapid development of robotic foundation models necessitates more efficient and intuitive methods for instruction and adaptation without extensive retraining.
This development offers a novel, low-cost way to steer robot behavior using readily available human video data, significantly accelerating robot deployment and adaptability.
Robots can now learn and adapt task-specific behaviors from human demonstrations at test time, reducing the need for costly post-deployment fine-tuning or new robot demonstrations.
- · Robot manufacturers
- · Robotics researchers
- · Automation industries
- · Traditional robot training methodologies
- · Companies reliant on extensive robot re-demonstrations
Increased versatility and accelerated deployment of robotic systems across various applications.
Reduced barriers to entry for new robotic applications and potentially a broader market for robotics.
Enhanced human-robot collaboration as robots become more adept at understanding and adapting to human-preferred behaviors.
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Read at arXiv cs.AI