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Publications

Displaying results 2021 to 2030 of 14899.

  1. Alexandros Paraschos; Christian Daniel; Jan Peters; Gerhard Neumann

    Using probabilistic movement primitives in robotics

    In: Autonomous Robots, Vol. 42, No. 3, Pages 529-551, Springer, 2018.

  2. Takayuki Osa; Jan Peters; Gerhard Neumann

    Hierarchical reinforcement learning of multiple grasping strategies with human instructions

    In: Advanced Robotics, Vol. 32, No. 18, Pages 955-968, Taylor & Francis Online, 2018.

  3. Alexandros Paraschos; Elmar Rueckert; Jan Peters; Gerhard Neumann

    Probabilistic movement primitives under unknown system dynamics

    In: Advanced Robotics, Vol. 32, No. 6, Pages 297-310, Taylor & Francis Online, 2018.

  4. Daniel Tanneberg; Elmar Rueckert; Jan Peters

    Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1911.00926, Pages 0-10, arXiv, 2019.

  5. Zhang-Wei Hong; Joni Pajarinen; Jan Peters

    Model-based Lookahead Reinforcement Learning

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1908.06012, Pages 0-10, arXiv, 2019.

  6. Zinan Liu; Arne Hitzmann; Shuhei Ikemoto; Svenja Stark; Jan Peters; Koh Hosoda

    Local Online Motor Babbling: Learning Motor Abundance of A Musculoskeletal Robot Arm

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1906.09013, Pages 0-10, arXiv, 2019.

  7. Riad Akrour; Joni Pajarinen; Jan Peters; Gerhard Neumann

    Projections for Approximate Policy Iteration Algorithms

    In: Kamalika Chaudhuri; Ruslan Salakhutdinov (Hrsg.). Proceedings of the 36th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2019), June 9-15, Long Beach, California, USA, Pages 181-190, Proceedings of Machine Learning Research, Vol. 97, PMLR, 2019.

  8. Michael Lutter; Christian Ritter; Jan Peters

    Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

    In: 7th International Conference on Learning Representations. International Conference on Learning Representations (ICLR-2019), May 6-9, New Orleans, LA, USA, OpenReview.net, 2019.

  9. Joe Watson; Hany Abdulsamad; Jan Peters

    Stochastic Optimal Control as Approximate Input Inference

    In: Leslie Pack Kaelbling; Danica Kragic; Komei Sugiura (Hrsg.). 3rd Annual Conference on Robot Learning, Proceedings. Conference on Robot Learning (CoRL-2019), October 30 - November 1, Osaka, Japan, Pages 697-716, Proceedings of Machine Learning Research, Vol. 100, PMLR, 2019.

  10. Mikko Lauri; Joni Pajarinen; Jan Peters

    Information Gathering in Decentralized POMDPs by Policy Graph Improvement

    In: Edith Elkind; Manuela Veloso; Noa Agmon; Matthew E. Taylor (Hrsg.). AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19), May 13-17, Montreal, QC, Canada, Pages 1143-1151, ISBN 978-1-4503-6309-9, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2019.

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