Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• The demand for enhanced dexterity in robotic operations is rapidly increasing.
• Dexterous manipulation using multi-fingered robotic hands is a key research area.
• Early approaches relied on model-based methods without learning.

Purpose of the Study:

• To provide a comprehensive review of techniques for dexterous manipulation with multi-fingered robotic hands.
• To highlight the evolution from model-based methods to reinforcement learning approaches.
• To summarize the current state-of-the-art, challenges, and future directions in the field.

Main Methods:

• Review of early model-based approaches for robotic hand manipulation.
• Analysis of recent research focusing on reinforcement learning (RL) and its variations.
• Synthesis of existing literature to map the field's progression.

Main Results:

• The field has evolved significantly, with reinforcement learning emerging as a dominant methodology.
• A clear progression from traditional control to data-driven learning methods is observed.
• Key challenges and promising future research avenues have been identified.

Conclusions:

• Reinforcement learning offers powerful solutions for achieving dexterous manipulation in robotic hands.
• Understanding the field's evolution is crucial for addressing current challenges.
• This review serves as a guide for future research in robotic manipulation.