Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextLearning robot behavior from data with deep reinforcement learning.
Levine studies how robots and other agents can learn behavior from data. The selected papers connect deep reinforcement learning and reusable skills with generalist robot policies that combine vision, language, and action.
10 papers
Selected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSoft Actor-Critic combines off-policy learning with an objective that rewards both successful actions and policy entropy.
Paper & contextRT-1 uses a robotics transformer trained on diverse real-world demonstrations to map camera images and instructions to robot actions, studying generalization and control at scale.
Paper & contextRT-2 transfers knowledge from vision-language models into robotic control by representing actions in a compatible format.
Paper & contextOpen X-Embodiment combines robotic data across embodiments to study generalization and reusable robot policies.
Paper & contextSelected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models, robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in robotics. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
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