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- W4320086601 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. The framework consists of a segmentation network (SN), a virtual sensor network (VSN), and a controller network (CN). Concretely, the VSN is trained to measure the pose of the unseen shape from a segmented image. After that, given the shape-agnostic pose measurement, the CN is trained to achieve generic peg-in-hole. Finally, when applying to real unseen holes, we only have to fine-tune the SN required by the simulated VSN+CN. To further minimize the transfer cost, we propose to automatically collect and annotate the data for the SN after one-minute human teaching. Simulated and real-world results are presented under the configurations of eye-to/in-hand. An electric vehicle charging system with the proposed policy inside achieves a 10/10 success rate in 2-3s, using only hundreds of auto-labeled samples for the SN transfer." @default.
- W4320086601 created "2023-02-12" @default.
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- W4320086601 date "2022-05-09" @default.
- W4320086601 modified "2023-10-10" @default.
- W4320086601 title "Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes" @default.
- W4320086601 doi "https://doi.org/10.48550/arxiv.2205.04297" @default.
- W4320086601 hasPublicationYear "2022" @default.
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