Survey of learning-based approaches for robotic in-hand manipulation

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Human dexterity is crucial for object manipulation.
• Robotic in-hand manipulation is essential for robots in human environments.
• Previous methods (programmed paths, analytical modeling) faced limitations in complex, uncertain scenarios.

Purpose of the Study:

• To survey the development of learning-based approaches for robotic in-hand manipulation.
• To explore current challenges and future opportunities in the field.
• To serve as an introduction for novices and a guide for advanced practitioners.

Main Methods:

• Review of learning-based approaches, including data-driven models and Reinforcement Learning (RL).
• Analysis of the evolution from traditional methods to modern learning techniques.
• Identification of key advancements and research trends in robotic manipulation.

Main Results:

• Learning-based methods, especially RL, show significant promise for achieving dexterous in-hand manipulation.
• RL enables solutions with minimal human guidance, overcoming limitations of prior approaches.
• The field is rapidly evolving, with increasing interest in data-driven and RL techniques.

Conclusions:

• Learning approaches, particularly RL, are pivotal for advancing robotic in-hand manipulation.
• Continued research is needed to address challenges and unlock new opportunities in complex manipulation tasks.
• This survey provides a comprehensive overview of the field's progress and future directions.