用视觉与本体感知结合的强化学习模型，提升机器人推物精准度。
Precision-Focused Reinforcement Learning Model for Robotic Object Pushing
- 基于视觉与本体感知的内存式强化学习模型
- 减少纠正动作次数，提升目标位置精度
- 适合需要高精度推物的机器人场景
非抓取式操作（如将物体推至目标位置）是机器人在日常生活中辅助人类的重要技能。然而，由于物体形状、尺寸、质量及摩擦系数等物理属性多样且常未知，导致物体易过度推送，需机器人快速绕行修正，尤其在高精度要求下更为显著。本文通过引入一种基于记忆的视觉-本体感知强化学习模型，改进现有技术，使机器人能更精确地将物体推至目标位置，同时减少纠正动作次数。
原文摘要 · Abstract (English)
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.
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