Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and friction. This can lead to the object overshooting its target position, requiring fast corrective movements of the robot around the object, especially in cases where objects need to be precisely pushed. In this paper, we improve the state-of-the-art by introducing a new memory-based vision-proprioception RL model to push objects more precisely to target positions using fewer corrective movements.

翻译：非抓取式操作（如将物体推动至期望目标位置）是机器人在日常场景中辅助人类的重要技能。然而，由于物体在形状、尺寸、质量和摩擦系数等物理属性上存在巨大差异且有时未知，该任务极具挑战性。这可能导致物体超过目标位置，从而需要机器人在物体周围进行快速校正运动，尤其在需要精确推动物体的情况下。本文通过引入一种新型基于记忆的视觉-本体感知强化学习模型，以更少的校正运动将物体更精确地推动至目标位置，从而改进了现有技术水平。