Arthur Allshire
Aleksei Petrenko | Aleksei Petrenko home page
Description: DeXtreme is a system to perform highly dextrous manipulation with robotic hands using scaled simulation data.
robotic (251) manipulation (226) reinforcement learning (165) dexterous (2)
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.
DeXtreme 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.
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.
Arthur Allshire
Aleksei Petrenko | Aleksei Petrenko home page