General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics. Deep reinforcement learning (RL) has shown great potential in solving robot manipulation tasks. However, existing RL policies have limited adaptability to environments with diverse dynamics properties, which is pivotal in solving many contact-rich manipulation tasks. In this work, we propose Contact-aware Online COntext Inference (COCOI), a deep RL method that encodes a context embedding of dynamics properties online using contact-rich interactions. We study this method based on a novel and challenging non-planar pushing task, where the robot uses a monocular camera image and wrist force torque sensor reading to push an object to a goal location while keeping it upright. We run extensive experiments to demonstrate the capability of COCOI in a wide range of settings and dynamics properties in simulation, and also in a sim-to-real transfer scenario on a real robot (Video: https://youtu.be/nrmJYksh1Kc)

中文翻译：

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COCOI：通用非平面推送的接触感知在线上下文推断

由于难以理解复杂的接触物理学，一般的接触丰富的操纵问题是机器人技术中的长期挑战。深度强化学习（RL）在解决机器人操纵任务方面显示出巨大潜力。但是，现有的RL策略对具有多种动力学特性的环境的适应性有限，这对于解决许多接触丰富的操纵任务至关重要。在这项工作中，我们提出了接触感知在线COntext推理（COCOI），这是一种深度RL方法，它使用接触丰富的交互作用在线编码动力学属性的上下文嵌入。我们基于一种新颖且具有挑战性的非平面推动任务来研究此方法，该机器人使用单眼相机图像和腕力扭矩传感器读数将物体推向目标位置，同时保持其直立。