VLD: Visual Learned Distance as an Interface for Reinforcement Learning Navigation
Overview
This research introduces Visual Learned Distance (VLD), a scalable framework for image-goal navigation that decouples perception learning from policy learning. The authors train a self-supervised distance-to-goal predictor on approximately 3,000 hours of internet-scale and simulated video data. An RL policy is then trained in simulation using privileged geometric distances perturbed by GeoNoise, a learned noise model that mimics the errors of the distance predictor. At deployment, the policy uses VLD predictions zero-shot without retraining. The study reports that with VLD inputs, GeoNoise-trained policies achieve 58.7% success on HM3D and 76.2% on Gibson, compared to 4.9% and 11.5% for policies trained with generic noise. On a physical robot, the VLD-driven policy succeeds in 88% of trials, outperforming a strong end-to-end image-goal baseline that achieves 60% success.
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Reported quantity · hours of internet-scale and simulated video: 3000 hours · Basis not reported
“al navigation that decouples perception learning from policy learning. We first train a self-supervised distance-to-goal predictor on about 3,000 hours of internet-scale and simulated video, providing a distance signal that a reinforcement learning (RL) policy can minimize. The RL policy is trained separately in simulation using privileged geometric distances perturbed by GeoNoise, a learned noise model that reproduc”
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- arXiv Robotics — research abstractsSignalVLD: Visual Learned Distance as an Interface for Reinforcement Learning Navigation
This research introduces Visual Learned Distance (VLD), a scalable framework for image-goal navigation that decouples perception learning from policy learning. The authors train a self-supervised distance-to-goal predictor on approximately 3,000 hours of internet-scale and simulated video data. An RL policy is then trained in simulation using privileged geometric distances perturbed by GeoNoise, a learned noise model that mimics the errors of the distance predictor. At deployment, the policy uses VLD predictions zero-shot without retraining. The study reports that with VLD inputs, GeoNoise-trained policies achieve 58.7% success on HM3D and 76.2% on Gibson, compared to 4.9% and 11.5% for policies trained with generic noise. On a physical robot, the VLD-driven policy succeeds in 88% of trials, outperforming a strong end-to-end image-goal baseline that achieves 60% success.
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