Precision-Focused Reinforcement Learning Model for Robotic Object Pushing
- URL: http://arxiv.org/abs/2411.08622v1
- Date: Wed, 13 Nov 2024 14:08:58 GMT
- Title: Precision-Focused Reinforcement Learning Model for Robotic Object Pushing
- Authors: Lara Bergmann, David Leins, Robert Haschke, Klaus Neumann,
- Abstract summary: Non-prehensile manipulation is an important skill for robots to assist humans in everyday situations.
We introduce a new memory-based vision-proprioception RL model to push objects more precisely to target positions.
- Score: 1.2374541748245842
- License:
- Abstract: 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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