Back to the Manifold: Recovering from Out-of-Distribution States-Reference-Cited by-同舟云学术

Back to the Manifold: Recovering from Out-of-Distribution States

Published:2022-10-23 Issue: Volume: Page:
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Container-title:2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Reichlin Alfredo¹,Marchetti Giovanni Luca¹,Yin Hang¹,Ghadirzadeh Ali²,Kragic Danica¹

Affiliation:

1. KTH Royal Institute of Technology

2. Stanford University

Funder

Swedish Research Council

Knut and Alice Wallenberg Foundation

European Research Council

H2020 CANOPIES

Publisher

IEEE

Link

Reference46 articles.

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2. Embed to control: A locally linear latent dynamics model for control from raw images;watter;Advances in neural information processing systems,2015

3. Learning to linearize under uncertainty;goroshin;Advances in neural information processing systems,2015

4. Stabilizing off-policy q-learning via bootstrapping error reduction;kumar;Advances in neural information processing systems,2019

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