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The data-driven approach to robot control has been gathering pace rapidly, yet generalization to unseen task domains remains a critical challenge.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
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Taskonomy: Disentangling task transfer learning
Amir R. Zamir, et al · 2018
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Gibson env: Real-world perception for embodied agents
Fei Xia, Amir Roshan Zamir, Zhi-Yang He, Alexander Sax, Jitendra Malik, and Silvio Savarese · 2018
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Learning to navigate using mid-level visual priors
Alexander Sax, et al · 2019
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Vr-goggles for robots: Real-to-sim domain adaptation for visual control
Jingwei Zhang, et al · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Habitat: A platform for embodied AI research
Manolis Savva, et al · 2019
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Robust policies via mid-level visual representations: An experimental study in manipulation and navigation
Bryan Chen, et al · 2020
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The foundation of efficient robot learning
Leslie Pack Kaelbling · 2020
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Learning dexterous in-hand manipulation
Marcin Andrychowicz, et al · 2020
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From machine learning to robotics: Challenges and opportunities for embodied intelligence
Nicholas Roy, et al · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, et al · 2021
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Bi-directional domain adaptation for sim2real transfer of embodied navigation agents
Joanne Truong, Sonia Chernova, and Dhruv Batra · 2021
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Deep visual navigation under partial observability
Bo Ai, Wei Gao, Vinay, and David Hsu · 2022
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Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
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