no code implementations • 7 Mar 2024 • Fabian Otto, Philipp Becker, Vien Ang Ngo, Gerhard Neumann
Existing off-policy reinforcement learning algorithms typically necessitate an explicit state-action-value function representation, which becomes problematic in high-dimensional action spaces.
1 code implementation • 31 Oct 2023 • Philipp Dahlinger, Philipp Becker, Maximilian Hüttenrauch, Gerhard Neumann
Before each update, it solves the trust region problem for an optimal step size, resulting in a more stable and faster optimization process.
1 code implementation • 11 Apr 2023 • Maximilian Xiling Li, Onur Celik, Philipp Becker, Denis Blessing, Rudolf Lioutikov, Gerhard Neumann
Learning skills by imitation is a promising concept for the intuitive teaching of robots.
no code implementations • 10 Feb 2023 • Philipp Becker, Sebastian Markgraf, Fabian Otto, Gerhard Neumann
Combining inputs from multiple sensor modalities effectively in reinforcement learning (RL) is an open problem.
1 code implementation • 17 Oct 2022 • Philipp Becker, Gerhard Neumann
We show that RSSMs use a suboptimal inference scheme and that models trained using this inference overestimate the aleatoric uncertainty of the ground truth system.
Model-based Reinforcement Learning reinforcement-learning +2
1 code implementation • 17 Oct 2022 • Niklas Freymuth, Nicolas Schreiber, Philipp Becker, Aleksandar Taranovic, Gerhard Neumann
We find that the geometric descriptors greatly help in generalizing to new task configurations and that combining them with our distribution-matching objective is crucial for representing and reproducing versatile behavior.
1 code implementation • ICLR 2022 • Vaisakh Shaj, Dieter Buchler, Rohit Sonker, Philipp Becker, Gerhard Neumann
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification.
no code implementations • 27 May 2022 • Moritz Reuss, Niels van Duijkeren, Robert Krug, Philipp Becker, Vaisakh Shaj, Gerhard Neumann
These models need to precisely capture the robot dynamics, which consist of well-understood components, e. g., rigid body dynamics, and effects that remain challenging to capture, e. g., stick-slip friction and mechanical flexibilities.
1 code implementation • 8 Dec 2021 • Onur Celik, Dongzhuoran Zhou, Ge Li, Philipp Becker, Gerhard Neumann
This local and incremental learning results in a modular MoE model of high accuracy and versatility, where both properties can be scaled by adding more components on the fly.
no code implementations • 16 Nov 2021 • Giao Nguyen-Quynh, Philipp Becker, Chen Qiu, Maja Rudolph, Gerhard Neumann
In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance.
no code implementations • 15 Nov 2021 • Niklas Freymuth, Philipp Becker, Gerhard Neumann
Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert.
1 code implementation • ICLR 2021 • Fabian Otto, Philipp Becker, Ngo Anh Vien, Hanna Carolin Ziesche, Gerhard Neumann
However, enforcing such trust regions in deep reinforcement learning is difficult.
2 code implementations • 20 Oct 2020 • Vaisakh Shaj, Philipp Becker, Dieter Buchler, Harit Pandya, Niels van Duijkeren, C. James Taylor, Marc Hanheide, Gerhard Neumann
We adopt a recent probabilistic recurrent neural network architecture, called Re-current Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions.
1 code implementation • ICLR 2020 • Philipp Becker, Oleg Arenz, Gerhard Neumann
Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes.
3 code implementations • 17 May 2019 • Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, James Taylor, Gerhard Neumann
In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often less scalable due to approximation errors.