  Optimizing behaviors for dexterous manipulation has been a longstanding
challenge in robotics, with a variety of methods from model-based control to
model-free reinforcement learning having been previously explored in
literature. Perhaps one of the most powerful techniques to learn complex
manipulation strategies is imitation learning. However, collecting and learning
from demonstrations in dexterous manipulation is quite challenging. The
complex, high-dimensional action-space involved with multi-finger control often
leads to poor sample efficiency of learning-based methods. In this work, we
propose 'Dexterous Imitation Made Easy' (DIME) a new imitation learning
framework for dexterous manipulation. DIME only requires a single RGB camera to
observe a human operator and teleoperate our robotic hand. Once demonstrations
are collected, DIME employs standard imitation learning methods to train
dexterous manipulation policies. On both simulation and real robot benchmarks
we demonstrate that DIME can be used to solve complex, in-hand manipulation
tasks such as 'flipping', 'spinning', and 'rotating' objects with the Allegro
hand. Our framework along with pre-collected demonstrations is publicly
available at https://nyu-robot-learning.github.io/dime.
