Dexterous Manipulation through Imitation Learning: A Survey
Shan An, Ziyu Meng, Chao Tang, Yuning Zhou, Tengyu Liu, Fangqiang Ding, Shufang Zhang, Yao Mu, Ran Song, Wei Zhang, Zeng-Guang Hou, Hong Zhang
TL;DR
This survey analyzes imitation-learning approaches for dexterous manipulation, detailing behavioral cloning, inverse reinforcement learning, GAIL, hierarchical and continual IL, and their applicability to complex, contact-rich tasks. It maps end-effector designs, teleoperation and video-based data collection, and the role of tactile sensing in enabling robust IL-based dexterous control. Key contributions include a taxonomy of IL methods, a comparative view of their trade-offs, and a roadmap addressing data generation, benchmarking, sim-to-real transfer, and safety for real-world deployment. The work emphasizes hybrid, model-based–data-driven integrations, multi-agent coordination, and representation learning as critical paths to scalable, generalizable dexterous manipulation. Overall, it provides a practical guide for researchers to advance IL-enabled dexterous robotics from laboratory demonstrations toward real-world applications.
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, 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.