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

2025-05-30

AI Summary

Dexterous robotic manipulation faces core challenges including high-dimensional control, limited training samples, and covariate shift. This paper systematically identifies the theoretical gaps in Interactive Imitation Learning (IIL) for dexterous manipulation and proposes a human-robot collaborative behavior refinement paradigm. Methodologically, it integrates behavioral cloning, inverse reinforcement learning, active querying, and online human feedback modeling, augmented with uncertainty-aware estimation and multi-task transfer mechanisms. Key contributions include: (1) a taxonomy of seven fundamental challenges; (2) distillation of twelve reusable technical modules; (3) formulation of four empirically verifiable research directions; and (4) the first structured IIL methodology framework tailored to dexterous manipulation. Empirical evaluation demonstrates substantial improvements in few-shot adaptability and policy generalization across diverse manipulation tasks.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 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 robot's 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.

Problem

• Addressing dexterous robotic manipulation challenges in humanoid robots
• Exploring interactive imitation learning for refining robot behavior
• Adapting existing methods to enhance dexterous manipulation skills

Innovation

• Interactive imitation learning enhances robot training
• Human feedback refines robotic behavior dynamically
• Adapting techniques from other robotic tasks