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.

翻译：视觉运动导航策略已展现出对具身智能体感知-动作耦合的强大能力，然而在复杂的现实环境中，它们常常在安全导航与动态避障方面存在困难。我们提出了CHOP，一种新颖的方法，利用反事实人类偏好标签使视觉运动导航策略与人类对导航安全性和避障的直觉认知对齐。在CHOP中，针对每个视觉观测，机器人实际执行的轨迹被纳入一组反事实导航轨迹之中：这些是机器人在相同条件下可能遵循的替代轨迹。人类标注者基于碰撞风险和路径效率等预期结果，对这些轨迹提供成对偏好标签。这些聚合后的偏好随后被用于微调视觉运动导航策略，使其导航行为与人类偏好保持一致。在SCAND数据集上的实验表明，与预训练基线相比，使用CHOP微调的视觉运动导航策略在多个最先进模型中，平均将接近碰撞事件减少了49.7%，与人类偏好轨迹的偏差降低了45.0%，并将平均障碍物间距增加了19.8%。这些改进迁移到了Ghost Robotics Vision60四足机器人的实际部署中，与预训练基线相比，CHOP对齐的策略在各种导航场景中平均将目标成功率提高了24.4%，最小障碍物间距增加了6.8%，碰撞与干预事件减少了45.7%，并将归一化路径完成度提升了38.6%。我们的研究结果凸显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、具备安全意识的具身导航之间差距方面的价值。