robotics
The Robot Predicted Whether Its Command Could Actually Happen
Social-WM learned the gap between nominal and realizable motion from egocentric video.
Summary
Social-WM learned the gap between nominal and realizable motion from egocentric video.
Social-WM models the future after the action that was actually realized, rather than assuming every commanded action executed fully. At planning time it imagines candidates and uses the discrepancy between nominal and realizable action as a safety signal. On Social-HM3D, the authors report 63.77% success with human collisions reduced to 21.67%, plus zero-shot transfer to Social-MP3D, without privileged human state or online reinforcement learning. The numbers come from simulation and do not establish real-world pedestrian safety.
Why it matters
Social-WM learned the gap between nominal and realizable motion from egocentric video.
Limits and context
- The numbers come from simulation and do not establish real-world pedestrian safety.
Key claims
Social-WM learned the gap between nominal and realizable motion from egocentric video.
Qualification: The numbers come from simulation and do not establish real-world pedestrian safety.
Evidence: source-2026-10-01-007
Sources
- arXiv preprint 2609.40177arXiv · primary research
Corrections
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