CHOP：反事实人类偏好标签改进视觉运动导航策略中的障碍物避让
CHOP: Counterfactual Human Preference Labels Improve Obstacle Avoidance in Visuomotor Navigation Policies
AI总结：
CHOP通过反事实人类偏好标签改进视觉运动导航策略的障碍物避让能力，显著提升安全性和导航效率。
摘要：
视觉运动导航策略在具身代理中展示了强大的感知-动作耦合性，但它们在复杂真实环境中安全导航和动态障碍物避让方面往往面临困难。我们引入了CHOP，一种新的方法，利用反事实人类偏好标签来对齐视觉运动导航策略，使其朝着人类对安全性和障碍物避让的直觉方向发展。在CHOP中，对于每个视觉观察，机器人的执行轨迹会被包含在一组反事实导航轨迹中：在相同条件下机器人可以采取的其他轨迹。人类标注者会根据预期结果如碰撞风险和路径效率，对这些轨迹进行成对偏好标注。这些汇总的偏好随后用于微调视觉运动导航策略，使策略的行为与人类偏好在导航中对齐。在SCAND数据集上的实验表明，使用CHOP微调的视觉运动导航策略将近碰撞事件减少了49.7%，将偏离人类偏好轨迹的幅度减少了45.0%，并且在多个最先进的模型上，平均障碍物清除率提高了19.8%。这些改进转移到现实世界部署中，如Ghost Robotics Vision60四足机器人上，其中CHOP对齐的策略在导航场景中将平均目标成功率提高了24.4%，最小障碍物清除度增加了6.8%，碰撞和干预事件减少了45.7%，并且平均路径完成度提高了38.6%。我们的结果强调了反事实偏好监督在弥合大规模视觉运动策略与人类对齐、安全意识的具身导航之间的差距方面的价值。
英文摘要：
Visuomotor navigation policies have shown strong perception-action coupling for embodied agents, yet they often struggle with safe navigation and dynamic obstacle avoidance in complex real-world environments. We introduce CHOP, a novel approach that leverages Counterfactual Human Preference Labels to align visuomotor navigation policies towards human intuition of safety 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 aggregated preferences are then used to fine-tune visuomotor navigation policies, aligning their behavior with human preferences in navigation. Experiments on the SCAND dataset show that visuomotor navigation policies fine-tuned with CHOP reduce near-collision events by 49.7%, decrease deviation from human-preferred trajectories by 45.0%, and increase average obstacle clearance by 19.8% on average across multiple state-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 to their pretrained baselines. Our results highlight the value of counterfactual preference supervision in bridging the gap between large-scale visuomotor policies and human-aligned, safety-aware embodied navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
