Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey

TL;DR

This survey reviews the challenges of dexterous robotic manipulation and explores how interactive imitation learning can address these issues by incorporating human feedback for improved learning efficiency and adaptability.

Contribution

It provides a comprehensive overview of current methods, highlights the potential of interactive imitation learning for dexterous manipulation, and discusses future research directions.

Findings

Interactive imitation learning has shown success in various robotic tasks.

Current applications to dexterous manipulation are limited but promising.

Identifies key challenges and gaps in existing methodologies.

Abstract

Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and interact with everyday objects, mastering dexterous manipulation is critical for real-world deployment. Traditional approaches, such as reinforcement learning and imitation learning, have made significant strides, but they often struggle due to the unique challenges of real-world dexterous manipulation, including high-dimensional control, limited training data, and covariate shift. This survey provides a comprehensive overview of these challenges and reviews existing learning-based methods for real-world dexterous manipulation, spanning imitation learning, reinforcement learning, and hybrid approaches. A promising yet underexplored direction is interactive imitation learning, where human feedback actively refines a robots behavior during training. While interactive imitation learning has shown success in various robotic tasks, its application to dexterous manipulation remains limited. To address this gap, we examine current interactive imitation learning techniques applied to other robotic tasks and discuss how these methods can be adapted to enhance dexterous manipulation. By synthesizing state-of-the-art research, this paper highlights key challenges, identifies gaps in current methodologies, and outlines potential directions for leveraging interactive imitation learning to improve dexterous robotic skills.