Dexterous Manipulation through Imitation Learning: A Survey
Journal:
arXiv
Published Date:
Apr 4, 2025
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.
