  While significant progress has been made on understanding hand-object
interactions in computer vision, it is still very challenging for robots to
perform complex dexterous manipulation. In this paper, we propose a new
platform and pipeline DexMV (Dexterous Manipulation from Videos) for imitation
learning. We design a platform with: (i) a simulation system for complex
dexterous manipulation tasks with a multi-finger robot hand and (ii) a computer
vision system to record large-scale demonstrations of a human hand conducting
the same tasks. In our novel pipeline, we extract 3D hand and object poses from
videos, and propose a novel demonstration translation method to convert human
motion to robot demonstrations. We then apply and benchmark multiple imitation
learning algorithms with the demonstrations. We show that the demonstrations
can indeed improve robot learning by a large margin and solve the complex tasks
which reinforcement learning alone cannot solve. More details can be found in
the project page: https://yzqin.github.io/dexmv
