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Graph Networks as Learnable Physics Engines for Inference and Control.

, , , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4467-4476. PMLR, (2018)

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Neural Probabilistic Motor Primitives for Humanoid Control., , , , , , , and . ICLR (Poster), OpenReview.net, (2019)Evaluating Model-Based Planning and Planner Amortization for Continuous Control., , , , , , , , , and 1 other author(s). ICLR, OpenReview.net, (2022)Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems., , , , , , , , and . ALIFE, page 103-110. MIT Press, (2019)Universal Humanoid Motion Representations for Physics-Based Control., , , , , , and . CoRR, (2023)Deep neuroethology of a virtual rodent., , , , , and . ICLR, OpenReview.net, (2020)DeepMind Control Suite., , , , , , , , , and 2 other author(s). CoRR, (2018)Universal Humanoid Motion Representations for Physics-Based Control., , , , , , and . ICLR, OpenReview.net, (2024)Reinforcement and Imitation Learning for Diverse Visuomotor Skills., , , , , , , , , and 1 other author(s). Robotics: Science and Systems, (2018)From Motor Control to Team Play in Simulated Humanoid Football., , , , , , , , , and 12 other author(s). CoRR, (2021)Neural probabilistic motor primitives for humanoid control., , , , , , , and . CoRR, (2018)