alishbaimran / Multi-Modal-ManipulationLinks
We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We train a policy in PyBullet (on the a Kuka LBR iiwa robot arm) using PPO for peg-in-hole tasks.
☆9Updated last year
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