no code implementations • 4 May 2024 • Yuan Zhang, Jasper Hoffmann, Joschka Boedecker
Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL).
no code implementations • 9 Apr 2024 • Mariella Dreissig, Florian Piewak, Joschka Boedecker
Safety-critical applications like autonomous driving call for robust 3D environment perception algorithms which can withstand highly diverse and ambiguous surroundings.
no code implementations • 1 Dec 2023 • Mehdi Naouar, Gabriel Kalweit, Anusha Klett, Yannick Vogt, Paula Silvestrini, Diana Laura Infante Ramirez, Roland Mertelsmann, Joschka Boedecker, Maria Kalweit
In recent years, several unsupervised cell segmentation methods have been presented, trying to omit the requirement of laborious pixel-level annotations for the training of a cell segmentation model.
no code implementations • 29 Nov 2023 • Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Roland Mertelsmann, Gabriel Kalweit, Joschka Boedecker
The field of antibody-based therapeutics has grown significantly in recent years, with targeted antibodies emerging as a potentially effective approach to personalized therapies.
no code implementations • 23 Nov 2023 • Hao Zhu, Brice De La Crompe, Gabriel Kalweit, Artur Schneider, Maria Kalweit, Ilka Diester, Joschka Boedecker
In advancing the understanding of decision-making processes, Inverse Reinforcement Learning (IRL) have proven instrumental in reconstructing animal's multiple intentions amidst complex behaviors.
1 code implementation • 22 Sep 2023 • Lukas AW Gemein, Robin T Schirrmeister, Joschka Boedecker, Tonio Ball
Furthermore, the brain age gap biomarker is not indicative of pathological EEG.
no code implementations • 4 Aug 2023 • Mariella Dreissig, Florian Piewak, Joschka Boedecker
We propose a metric to measure the confidence calibration quality of a semantic segmentation model with respect to individual classes.
no code implementations • 18 Jul 2023 • Suresh Guttikonda, Jan Achterhold, Haolong Li, Joschka Boedecker, Joerg Stueckler
Terrain properties such as friction coefficients may vary over time depending on the location of the robot.
1 code implementation • 8 May 2023 • Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold, Wolfram Burgard, Joschka Boedecker, Abhinav Valada
We employ our method to learn challenging multi-object robot manipulation tasks from wrist camera observations and demonstrate superior utility for policy learning compared to other representation learning techniques.
no code implementations • 13 Apr 2023 • Mariella Dreissig, Dominik Scheuble, Florian Piewak, Joschka Boedecker
The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception for autonomous vehicles.
no code implementations • 3 Apr 2023 • Andrea Ghezzi, Jasper Hoffman, Jonathan Frey, Joschka Boedecker, Moritz Diehl
This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning.
no code implementations • 29 Mar 2023 • Mehdi Naouar, Gabriel Kalweit, Ignacio Mastroleo, Philipp Poxleitner, Marc Metzger, Joschka Boedecker, Maria Kalweit
In this work, we put the focus back on tumor localization in form of a patch-level classification task and take up the setting of so-called coarse annotations, which provide greater training supervision while remaining feasible from a clinical standpoint.
no code implementations • 30 Jan 2023 • Yuan Zhang, Joschka Boedecker, Chuxuan Li, Guyue Zhou
Model Predictive Control (MPC) is attracting tremendous attention in the autonomous driving task as a powerful control technique.
1 code implementation • 6 Dec 2022 • Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad, Joschka Boedecker, Moritz Diehl, Jan Swevers
The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.
no code implementations • 13 Oct 2022 • Mariella Dreissig, Florian Piewak, Joschka Boedecker
The calibration of deep learning-based perception models plays a crucial role in their reliability.
1 code implementation • 19 Sep 2022 • Erick Rosete-Beas, Oier Mees, Gabriel Kalweit, Joschka Boedecker, Wolfram Burgard
Concretely, we combine a low-level policy that learns latent skills via imitation learning and a high-level policy learned from offline reinforcement learning for skill-chaining the latent behavior priors.
1 code implementation • 5 Jul 2022 • Yuan Zhang, Jianhong Wang, Joschka Boedecker
To deal with unknown uncertainty sets, we further propose a novel adversarial approach to generate them based on the value function.
no code implementations • 10 Apr 2022 • Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay, Zoe Jaeckel, Florian Steenbergen, Stefanie Hardung, Thomas Brox, Ilka Diester, Joschka Boedecker
However, since generally there is a strong connection between learning of subjects and their expectations on long-term rewards, we propose NeuRL, an inverse reinforcement learning approach that (1) extracts an intrinsic reward function from collected trajectories of a subject in closed form, (2) maps neural signals to this intrinsic reward to account for long-term dependencies in the behavior and (3) predicts the simulated behavior for unseen neural signals by extracting Q-values and the corresponding Boltzmann policy based on the intrinsic reward values for these unseen neural signals.
no code implementations • 21 Mar 2022 • Branka Mirchevska, Moritz Werling, Joschka Boedecker
Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge.
1 code implementation • 1 Mar 2022 • Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker, Wolfram Burgard
Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal.
1 code implementation • 24 Nov 2021 • Nicolai Dorka, Tim Welschehold, Joschka Boedecker, Wolfram Burgard
Accurate value estimates are important for off-policy reinforcement learning.
no code implementations • 29 Sep 2021 • Gabriel Kalweit, Maria Kalweit, Joschka Boedecker
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control.
no code implementations • 8 Jun 2021 • Alireza Ranjbar, Ngo Anh Vien, Hanna Ziesche, Joschka Boedecker, Gerhard Neumann
We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty.
no code implementations • 6 Dec 2020 • Branka Mirchevska, Maria Hügle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds.
no code implementations • 21 Oct 2020 • Maria Kalweit, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
Challenging problems of deep reinforcement learning systems with regard to the application on real systems are their adaptivity to changing environments and their efficiency w. r. t.
no code implementations • 14 Aug 2020 • Maria Hügle, Gabriel Kalweit, Thomas Huegle, Joschka Boedecker
Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e. g. by finding new disease phenotypes or performing individual disease prediction.
2 code implementations • NeurIPS 2020 • Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
In this work, we introduce a novel class of algorithms that only needs to solve the MDP underlying the demonstrated behavior once to recover the expert policy.
no code implementations • 20 Mar 2020 • Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
We analyze the advantages of Constrained Q-learning in the tabular case and compare Constrained DQN to reward shaping and Lagrangian methods in the application of high-level decision making in autonomous driving, considering constraints for safety, keeping right and comfort.
1 code implementation • 11 Feb 2020 • Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball
The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks.
no code implementations • 30 Sep 2019 • Gabriel Kalweit, Maria Huegle, Joschka Boedecker
We prove that the combination of these short- and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm.
no code implementations • 30 Sep 2019 • Maria Huegle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component.
no code implementations • 25 Sep 2019 • Gabriel Kalweit, Maria Huegle, Joschka Boedecker
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control.
no code implementations • 25 Jul 2019 • Maria Huegle, Gabriel Kalweit, Branka Mirchevska, Moritz Werling, Joschka Boedecker
In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent.
1 code implementation • 27 Jun 2019 • Torsten Koller, Felix Berkenkamp, Matteo Turchetta, Joschka Boedecker, Andreas Krause
We evaluate the resulting algorithm to safely explore the dynamics of an inverted pendulum and to solve a reinforcement learning task on a cart-pole system with safety constraints.
no code implementations • 18 Mar 2019 • Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker, Wolfram Burgard
Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration.
no code implementations • 20 Oct 2018 • Fereshteh Lagzi, Tonio Ball, Joschka Boedecker
This criterion is based on the convergence of the neural dynamics in the last two successive layers of the residual block.
no code implementations • 12 Jun 2018 • Maria Hügle, Simon Heller, Manuel Watter, Manuel Blum, Farrokh Manzouri, Matthias Dümpelmann, Andreas Schulze-Bonhage, Peter Woias, Joschka Boedecker
Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation.
no code implementations • 1 Feb 2018 • Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard
In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks.
no code implementations • 20 Jul 2017 • Felix Burget, Lukas Dominique Josef Fiederer, Daniel Kuhner, Martin Völker, Johannes Aldinger, Robin Tibor Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball, Wolfram Burgard
As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time.
1 code implementation • 29 Jun 2017 • Jingwei Zhang, Lei Tai, Ming Liu, Joschka Boedecker, Wolfram Burgard
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments.
Reinforcement Learning (RL) Simultaneous Localization and Mapping
1 code implementation • 21 Dec 2016 • Lei Tai, Jingwei Zhang, Ming Liu, Joschka Boedecker, Wolfram Burgard
We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning.
no code implementations • 16 Dec 2016 • Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard
We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances.
1 code implementation • NeurIPS 2015 • Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller
We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images.
no code implementations • 6 Sep 2013 • Oliver Obst, Joschka Boedecker
We review attempts that have been made towards understanding the computational properties and mechanisms of input-driven dynamical systems like RNNs, and reservoir computing networks in particular.