Dexterous Manipulation through Imitation Learning: A Survey

2025-04-04

AI Summary

Multi-fingered robotic hands suffer from poor generalization, low data efficiency, and difficulty in reward design for dexterous manipulation in unstructured environments. Method: This paper presents a systematic survey of imitation learning (IL) for dexterous manipulation, introducing the first comprehensive taxonomy covering behavior cloning, inverse reinforcement learning, adversarial imitation learning, and vision-proprioception joint modeling—synthesizing over 100 works. Contribution/Results: We propose a unified framework identifying three core challenges: cross-task generalization, sparse demonstration utilization, and contact dynamics modeling. We analyze performance bottlenecks, unify evaluation protocols, and highlight emerging directions—including neural motor priors, multimodal demonstration fusion, and sim-to-real transfer. This work provides the first systematic guideline for algorithm design and experimental deployment in dexterous robotic manipulation via imitation learning.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain.

Problem

• Surveying imitation learning for dexterous robotic manipulation
• Addressing challenges in high-dimensional contact dynamics
• Reducing reliance on large-scale trial-and-error training

Innovation

• Imitation learning for dexterous robotic manipulation
• Bypassing explicit modeling with expert demonstrations
• Capturing fine-grained coordination and contact dynamics