Publications of Daniel Tanneberg

Journal Papers

Tanneberg, D.; Ploeger, K.; Rueckert, E.; Peters, J. (2021). SKID RAW: Skill Discovery from Raw Trajectories, IEEE Robotics and Automation Letters (RA-L).   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Rueckert, E.; Peters, J. (2020). Evolutionary training and abstraction yields algorithmic generalization of neural computers, Nature Machine Intelligence, 2, 12, pp.753-763.   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Peters, J.; Rueckert, E. (2019). Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks, Neural Networks, 109, pp.67-80.   Download Article [PDF]   BibTeX Reference [BibTex]

van Hoof, H.; Tanneberg, D.; Peters, J. (2017). Generalized Exploration in Policy Search, Machine Learning (MLJ), 106, 9-10, pp.1705-1724.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.; Kappel, D.; Tanneberg, D.; Pecevski, D; Peters, J. (2016). Recurrent Spiking Networks Solve Planning Tasks, Nature PG: Scientific Reports, 6, 21142, Nature Publishing Group.   Download Article [PDF]   BibTeX Reference [BibTex]

Conference and Workshop Papers

Keller, L.; Tanneberg, D.; Stark, S.; Peters, J. (2020). Model-Based Quality-Diversity Search for Efficient Robot Learning, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]

Delfosse, Q.; Stark, S.; Tanneberg, D.; Santucci, V. G.; Peters, J. (2019). Open-Ended Learning of Grasp Strategies using Intrinsically Motivated Self-Supervision, Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals, Proceedings of the Conference on Robot Learning (CoRL).   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with Stochastic Recurrent Neural Networks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Thiem, S.; Stark, S.; Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Simulation of the underactuated Sake Robotics Gripper in V-REP, Workshop at the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Sharma, D.; Tanneberg, D.; Grosse-Wentrup, M.; Peters, J.; Rueckert, E. (2016). Adaptive Training Strategies for BCIs, Cybathlon Symposium.   Download Article [PDF]   BibTeX Reference [BibTex]

Friess, T.; Fiebig, K.H.; Sharma, D.; Faber, N.; Hesse, T.; Tanneberg, D.; Peters, J.; Grosse-Wentrup, M. (2016). Personalized Brain-Computer Interfaces for Non-Laboratory Environments, Cybathlon Symposium.   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Paraschos, A.; Peters, J.; Rueckert, E. (2016). Deep Spiking Networks for Model-based Planning in Humanoids, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Books, Book Chapters & Theses

Tanneberg, D. (2020). Understand-Compute-Adapt: Neural Networks for Intelligent Agents, Ph.D. Thesis.   BibTeX Reference [BibTex]

Tanneberg, D. (2015). Spiking Neural Networks Solve Robot Planning Problems, Master Thesis.   Download Article [PDF]   BibTeX Reference [BibTex]

  

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