Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes 事件

Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes arXiv:2205.04297v2 Announce Type: replace-cross Abstract: This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy to the design of a fast-adaptable perception module and a simulated generic policy module.