用不确定性感知提升机器人在遮挡下的非抓取操作成功率
Uncertainty-Aware Non-Prehensile Manipulation with Mobile Manipulators under Object-Induced Occlusion
- 通过预测碰撞概率分布，同时建模风险与不确定性
- 在遮挡环境下成功率达基线3倍，显著降低碰撞
- 适合需要自主感知的移动操作机器人场景
利用机载传感器进行非抓取操作面临根本挑战：被操纵物体遮挡传感器视野，产生遮挡区域可能导致碰撞。我们提出CURA-PPO，一种强化学习框架，通过显式建模部分可观测下的不确定性来应对这一问题。通过预测碰撞可能性为分布，我们提取风险与不确定性以指导机器人动作。不确定性项鼓励主动感知，实现操纵与信息获取同步进行，以解决遮挡问题。结合捕捉观测可靠性的置信度图，该方法可在严重传感器遮挡下实现安全导航。在不同物体尺寸和障碍物配置下的大量实验表明，CURA-PPO相比基线最高提升3倍成功率，且学习到的行为能有效处理遮挡。本方法为仅使用机载传感的复杂环境自主操作提供了实用解决方案。
原文摘要 · Abstract (English)
Non-prehensile manipulation using onboard sensing presents a fundamental challenge: the manipulated object occludes the sensor's field of view, creating occluded regions that can lead to collisions. We propose CURA-PPO, a reinforcement learning framework that addresses this challenge by explicitly modeling uncertainty under partial observability. By predicting collision possibility as a distribution, we extract both risk and uncertainty to guide the robot's actions. The uncertainty term encourages active perception, enabling simultaneous manipulation and information gathering to resolve occlusions. When combined with confidence maps that capture observation reliability, our approach enables safe navigation despite severe sensor occlusion. Extensive experiments across varying object sizes and obstacle configurations demonstrate that CURA-PPO achieves up to 3X higher success rates than the baselines, with learned behaviors that handle occlusions. Our method provides a practical solution for autonomous manipulation in cluttered environments using only onboard sensing.
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