通过机器人中心池化提升端到端视觉运动策略学习中的身体所有权
Raising Body Ownership in End-to-End Visuomotor Policy Learning via Robot-Centric Pooling
AI总结：
提出机器人中心池化方法，通过突出与本体感觉相关的图像区域，增强端到端视觉运动策略对干扰物和像素偏移的鲁棒性，无需额外数据收集。
摘要：
我们提出了机器人中心池化（RcP），一种新颖的池化方法，旨在通过使机器人能够与相似实体或其周围环境区分开来，增强端到端视觉运动策略。给定图像-本体感觉对，RcP通过突出与机器人本体感觉状态相关的图像区域来引导图像特征的聚合，从而提取机器人中心的图像表示用于策略学习。利用对比学习技术，RcP与现有的视觉运动策略学习框架无缝集成，并使用相同的数据集与策略联合训练，无需额外收集涉及自我干扰物的数据。我们在模拟和真实世界环境中使用到达任务评估了所提出的方法。结果表明，RcP显著增强了策略对各种未见干扰物（包括位于不同位置的自我干扰物）的鲁棒性。此外，RcP固有的机器人中心特性使得学习到的策略比基线对激进像素偏移具有更强的鲁棒性。
英文摘要：
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.
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。
