DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
arxiv(2022)
摘要
Recent work has demonstrated the ability of deep reinforcement learning (RL)
algorithms to learn complex robotic behaviours in simulation, including in the
domain of multi-fingered manipulation. However, such models can be challenging
to transfer to the real world due to the gap between simulation and reality. In
this paper, we present our techniques to train a) a policy that can perform
robust dexterous manipulation on an anthropomorphic robot hand and b) a robust
pose estimator suitable for providing reliable real-time information on the
state of the object being manipulated. Our policies are trained to adapt to a
wide range of conditions in simulation. Consequently, our vision-based policies
significantly outperform the best vision policies in the literature on the same
reorientation task and are competitive with policies that are given privileged
state information via motion capture systems. Our work reaffirms the
possibilities of sim-to-real transfer for dexterous manipulation in diverse
kinds of hardware and simulator setups, and in our case, with the Allegro Hand
and Isaac Gym GPU-based simulation. Furthermore, it opens up possibilities for
researchers to achieve such results with commonly-available, affordable robot
hands and cameras. Videos of the resulting policy and supplementary
information, including experiments and demos, can be found at
https://dextreme.org/
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关键词
manipulation,simulation,in-hand
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