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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Interactive imitation learning for dexterous robotic manipulation: challenges and perspectives-a survey.

Edgar Welte¹, Rania Rayyes¹

¹AI and Robotics (AIR), Institute of Material Handling and Logistics (IFL), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

Frontiers in Robotics and AI

|January 5, 2026

Summary

This survey explores learning-based methods for humanoid robot dexterous manipulation. Interactive imitation learning, using human feedback, shows promise for improving robot skills in complex tasks.

Keywords:

dexterous manipulation human-in-the-loop learning imitation learning interactive learning learning from demonstration review

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Dexterous manipulation is vital for humanoid robots in human environments.
Traditional methods like reinforcement learning and imitation learning face challenges like high-dimensional control and limited data.
Real-world deployment requires adaptable and sample-efficient learning.

Purpose of the Study:

To provide a comprehensive overview of learning-based methods for dexterous manipulation.
To identify challenges in current approaches for real-world humanoid robot manipulation.
To explore the potential of interactive imitation learning for enhancing robotic dexterity.

Main Methods:

Review of existing literature on imitation learning, reinforcement learning, and hybrid approaches.
Analysis of interactive imitation learning techniques in other robotic domains.
Synthesis of state-of-the-art research to identify research gaps and future directions.

Main Results:

Existing methods struggle with the complexities of real-world dexterous manipulation.
Interactive imitation learning is an underexplored but promising avenue.
Adaptation of interactive imitation learning methods can enhance robotic manipulation skills.

Conclusions:

Dexterous manipulation is a key challenge for humanoid robots.
Interactive imitation learning offers a novel approach to improve robotic dexterity.
Further research is needed to adapt and apply interactive imitation learning to complex manipulation tasks.