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Imitation learning (IL) and reinforcement learning (RL) each offer distinct advantages for robotics policy learning: IL provides stable learning from demonstrations, and RL promotes generalization through exploration.
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Herbert Robbins and Sutton Monro · 1951
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Introductory Lectures on Convex Optimization: A Basic Course
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
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Multi-task learning as multi-objective optimization
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Scalable deep reinforcement learning for vision-based robotic manipulation
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Optimization methods for large-scale machine learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2018
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Language models are few-shot learners
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What matters in learning from offline human demonstrations for robot manipulation
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A survey on deep reinforcement learning algorithms for robotic manipulation
Dong Han, Beni Mulyana, Vladimir Stankovic, and Samuel Cheng · 2023
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Mastering diverse domains through world models
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Policy representation via diffusion probability model for reinforcement learning
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Mobile aloha: Learning bimanual mobile manipulation using low-cost whole-body teleoperation
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π \pi 0: A vision-language-action flow model for general robot control, 2024
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Rt-1: Robotics transformer for real-world control at scale
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Diffusion policy: Visuomotor policy learning via action diffusion
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Efficient online reinforcement learning with offline data
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