Adaptive Vision Strategies for Manipulation in Cluttered Environments: a Reinforcement Learning Perspective

EasyChair Preprint no. 11978

Abstract

Manipulating 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

@Booklet{EasyChair:11978,
  author = {Jane Smith and Robert Thomas},
  title = {Adaptive Vision Strategies for Manipulation in Cluttered Environments: a Reinforcement Learning Perspective},
  howpublished = {EasyChair Preprint no. 11978},

  year = {EasyChair, 2024}}