ロボット中心プーリングによる身体所有感を高めたEnd-to-End視覚運動方策学習
Raising Body Ownership in End-to-End Visuomotor Policy Learning via Robot-Centric Pooling
画像特徴をロボットの固有感覚状態と相関する領域に集約するRobot-centric Poolingを提案し、自己や周囲の類似物に頑健な視覚運動方策を実現した。
著者: Zheyu Zhuang, Ville Kyrki, Danica Kragic
分類: cs.RO
原文アブストラクト
We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomotor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an image-proprioception pair, RcP guides the aggregation of image features by highlighting image regions correlating with the robot's proprioceptive states, thereby extracting robot-centric image representations for policy learning. Leveraging contrastive learning techniques, RcP integrates seamlessly with existing visuomotor policy learning frameworks and is trained jointly with the policy using the same dataset, requiring no extra data collection involving self-distractors. We evaluate the proposed method with reaching tasks in both simulated and real-world settings. The results demonstrate that RcP significantly enhances the policies' robustness against various unseen distractors, including self-distractors, positioned at different locations. Additionally, the inherent robot-centric characteristic of RcP enables the learnt policy to be far more resilient to aggressive pixel shifts compared to the baselines.
