Download PDFOpen PDF in browserAdaptive Vision Strategies for Manipulation in Cluttered Environments: a Reinforcement Learning PerspectiveEasyChair Preprint 119787 pages•Date: February 7, 2024AbstractManipulating objects in cluttered environments presents a formidable challenge for robotic systems, requiring them to navigate through occlusions and dynamically changing scenes. In this paper, we propose a novel approach to address this challenge by integrating reinforcement learning with adaptive vision strategies tailored for manipulation tasks in cluttered environments. This research represents a significant step towards the development of intelligent robotic systems capable of autonomously navigating and manipulating objects in complex cluttered environments. Keyphrases: Effective, Robotic, vision
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