arXiv:2605.31196v1 Announce Type: cross Abstract: Safe human--robot collaboration requires more than visual description: a monitor must determine whether the robot body is safely separated, already colliding with the scene or a person, or about to collide. We call this capability collision grounding: binding visual observations to robot body geometry, camera viewpoint, scene layout, human proximity, and temporal motion in order to infer present and imminent contact. We introduce TouchSafeBench, a physics-grounded benchmark for evaluating collision grounding in vision-language models (VLMs). Bu
The increasing deployment of robots in human environments necessitates robust safety mechanisms, pushing researchers to develop benchmarks for collision avoidance in advanced AI models.
Ensuring safe human-robot interaction is critical for broad robot adoption, impacting industries from manufacturing to healthcare and consumer services.
This research introduces a standardized method for evaluating collision grounding, which will accelerate the development of safer vision-language models for robotic applications.
- · Robotics companies
- · AI safety researchers
- · Human-robot collaboration sectors
- · Companies with unsafe robot deployments
- · Less robust AI safety methodologies
Improved safety protocols for robots deployed in human environments will emerge, reducing accidents and enhancing public trust.
The widespread adoption of safer robots could unlock new markets and applications where human interaction was previously deemed too risky.
Higher levels of trust and capability could lead to legal and ethical frameworks that enable greater robot autonomy in complex, dynamic human settings.
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Read at arXiv cs.AI