Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments
arXiv:2607.07885v1 Announce Type: cross Abstract: Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-scale robot-specific training data and simulation-based policies are impractical. We present a data-efficient, interpretable method for vision-based dynamic obstacle avoidance that operates entirely on real-world data, avoiding the sim-to-real transfer problem inherent in simulation-trained policies. Our approach leverages UniDepth, a large pretrained monocular depth estimation model, to produce dens
The increasing computational power and availability of large pretrained vision models like UniDepth are enabling more sophisticated real-world robotic applications without extensive custom training.
This development addresses a critical barrier for autonomous systems in unstructured environments, accelerating the deployment of robots in complex settings by reducing the need for environment-specific training.
The reliance on simulation-to-real transfer is diminished, allowing for more robust and data-efficient navigation for robots operating in dynamic, real-world conditions.
- · Autonomous robotics companies
- · Logistics and delivery sectors
- · Defense contractors
- · AI model developers
- · Companies reliant solely on simulation-based training for robotics
- · Traditional obstacle avoidance sensor manufacturers
More widespread and reliable deployment of autonomous mobile robots in diverse outdoor settings.
Increased pressure on regulatory bodies to establish safety standards for autonomous systems operating in public and complex environments.
Potential for an acceleration in the development of general-purpose robots as vision models become increasingly capable of real-world scene understanding.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI