CHOP: Counterfactual Human Preference Labels Improve Obstacle Avoidance in Visuomotor Navigation Policies
Overview
This paper presents CHOP, a novel approach that utilizes counterfactual human preference labels to align visuomotor navigation policies with human intuition regarding safety and obstacle avoidance.
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Event evidence and corrections
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Data origin: robot trajectories
“fety and obstacle avoidance in navigation. In CHOP, for each visual observation, the robot's executed trajectory is included among a set of counterfactual navigation trajectories: alternative trajectories the robot could have followed under identical conditions. Human annotators provide pairwise preference labels over these trajectories based on anticipated outcomes such as collision risk and path efficiency. These a”
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Reported quantity · Reported success rate: 24.4 percent · reported trials
“e-of-the-art models, compared to their pretrained baselines. These improvements transfer to real-world deployments on a Ghost Robotics Vision60 quadruped, where CHOP-aligned policies improve average goal success rates by 24.4%, increase minimum obstacle clearance by 6.8%, reduce collision and intervention events by 45.7%, and improve normalized path completion by 38.6% on average across navigation scenarios, compared”
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- arXiv Robotics — research abstractsSignalCHOP: Counterfactual Human Preference Labels Improve Obstacle Avoidance in Visuomotor Navigation Policies
This paper presents CHOP, a novel approach that utilizes counterfactual human preference labels to align visuomotor navigation policies with human intuition regarding safety and obstacle avoidance.
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