让机器人在视觉动作学习中更清楚识别自身，提升抗干扰能力。
Raising Body Ownership in End-to-End Visuomotor Policy Learning via Robot-Centric Pooling
- 通过机器人中心池化，聚焦与本体感知相关的图像区域。
- 在模拟和真实场景中显著提升对未知干扰的鲁棒性。
- 无需额外数据，适合强化学习中的端到端视觉动作策略。
我们提出机器人中心池化（RcP），一种新型池化方法，旨在通过区分机器人与其相似物体或周围环境，增强端到端视觉动作策略的学习效果。给定图像-本体感知对，RcP通过突出与机器人本体感知状态相关的图像区域，引导特征聚合，从而提取以机器人为中心的图像表征用于策略学习。结合对比学习技术，RcP可无缝集成至现有视觉动作策略框架中，并与策略联合训练，无需额外采集包含自干扰物的数据。我们在模拟和真实世界中的抓取任务上进行了评估，结果表明，RcP显著提升了策略对各种未见干扰物（包括不同位置的自干扰物）的鲁棒性。此外，其固有的机器人中心特性使学习到的策略对剧烈像素偏移也更具韧性，优于基线方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。