Tim Seyde
Tim Seyde
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Zitiert von
Zitiert von
Good Posture, Good Balance: Comparison of Bioinspired and Model-Based Approaches for Posture Control of Humanoid Robots
C Ott, B Henze, G Hettich, TN Seyde, MA Roa, V Lippi, T Mergner
IEEE Robotics & Automation Magazine 23 (1), 22-33, 2016
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
W Schwarting, T Seyde, I Gilitschenski, L Liebenwein, R Sander, ...
Inclusion of Angular Momentum During Planning for Capture Point Based Walking
T Seyde, A Shrivastava, J Englsberger, S Bertrand, J Pratt, RJ Griffin
2018 IEEE International Conference on Robotics and Automation (ICRA), 1791-1798, 2018
Locomotion Planning through a Hybrid Bayesian Trajectory Optimization
T Seyde, J Carius, R Grandia, F Farshidian, M Hutter
2019 International Conference on Robotics and Automation (ICRA), 5544-5550, 2019
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
T Seyde, I Gilitschenski, W Schwarting, B Stellato, M Riedmiller, ...
Advances in Neural Information Processing Systems 34, 2021
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
T Seyde*, W Schwarting*, S Karaman, D Rus
5th Annual Conference on Robot Learning, 2021
Interpretable Autonomous Flight Via Compact Visualizable Neural Circuit Policies
P Tylkin, TH Wang, K Palko, R Allen, HC Siu, D Wrafter, T Seyde, A Amini, ...
IEEE Robotics and Automation Letters 7 (2), 3265-3272, 2022
Learning to Plan via Deep Optimistic Value Exploration
T Seyde*, W Schwarting*, S Karaman, DL Rus
Learning for Dynamics and Control, 815--825, 2020
Interpreting Neural Policies with Disentangled Tree Representations
TH Wang, W Xiao, T Seyde, R Hasani, D Rus
arXiv preprint arXiv:2210.06650, 2022
Autonomous Flight Arcade Challenge: Single-and Multi-Agent Learning Environments for Aerial Vehicles
P Tylkin, TH Wang, T Seyde, K Palko, R Allen, A Amini, D Rus
Proceedings of the 21st International Conference on Autonomous Agents and …, 2022
Strength Through Diversity: Robust Behavior Learning via Mixture Policies
T Seyde, W Schwarting, I Gilitschenski, M Wulfmeier, D Rus
Conference on Robot Learning, 1144-1155, 2022
Solving Continuous Control via Q-learning
T Seyde, P Werner, W Schwarting, I Gilitschenski, M Riedmiller, D Rus, ...
arXiv preprint arXiv:2210.12566, 2022
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
R Sander, W Schwarting, T Seyde, I Gilitschenski, S Karaman, D Rus
Learning for Dynamics and Control Conference, 954-967, 2022
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