器用な模倣を簡単に：効率的な巧緻操作のための学習ベースフレームワーク
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation
単一RGBカメラで人間の操作を観察してロボットハンドを遠隔操作し、そのデモから模倣学習で巧緻なハンド内操作（物体のフリップ・回転など）を実現するフレームワークDIMEを提案した論文。
著者: Sridhar Pandian Arunachalam, Sneha Silwal, Ben Evans, Lerrel Pinto
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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.
