Review of Learning-Based Robotic Manipulation in Cluttered Environments.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Robotic manipulation, involving intelligent interaction with objects, is crucial for tasks too dangerous or difficult for humans.
• Dexterous manipulation requires sophisticated planning and control of robotic hands and arms.
• Object manipulation in cluttered environments presents significant challenges in robotics.

Purpose of the Study:

• To review and analyze studies on learning-based object manipulation in cluttered environments.
• To provide insights into object manipulation using deep reinforcement learning (deep RL) in dense clutter.
• To identify challenges and recommendations for overcoming obstacles in robotic manipulation.

Main Methods:

• Surveying existing literature on learning-based object manipulation.
• Investigating various aspects including applications, techniques, challenges, and solutions.
• Categorizing deep RL-based robotic manipulation tasks in clutter into object removal, assembly/rearrangement, and retrieval/singulation.

Main Results:

• Identified key challenges in robotic manipulation within cluttered settings.
• Examined diverse applications and techniques, particularly deep RL.
• Categorized manipulation tasks to provide a structured overview.

Conclusions:

• Deep reinforcement learning shows promise for complex object manipulation in clutter.
• Further research is needed to address current challenges and enhance robotic capabilities.
• This review aims to guide future research and establish best practices in the field.