234 citations · 938 across the 65 of their papers we have counts for
8 papers · 1 filter
A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing
Rudolf Reiter, Jasper Hoffmann, Joschka Boedecker +1
We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample-efficient within a simulation environment. A high-level p…
On the calibration of underrepresented classes in LiDAR-based semantic segmentation
Mariella Dreissig, Florian Piewak, Joschka Boedecker
The calibration of deep learning-based perception models plays a crucial role in their reliability. Our work focuses on a class-wise evaluation of several model's confidence perfor…
Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems
Marieke Wesselkamp, Niklas Moser, Maria Kalweit +2
Despite deep-learning being state-of-the-art for data-driven model predictions, it has not yet found frequent application in ecology. Given the low sample size typical in many envi…
Latent Plans for Task-Agnostic Offline Reinforcement Learning
Erick Rosete-Beas, Oier Mees, Gabriel Kalweit +2
Everyday tasks of long-horizon and comprising a sequence of multiple implicit subtasks still impose a major challenge in offline robot control. While a number of prior methods aime…
Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization
Yuan Zhang, Jianhong Wang, Joschka Boedecker
Reinforcement learning (RL) is recognized as lacking generalization and robustness under environmental perturbations, which excessively restricts its application for real-world rob…
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay +6
Current neural decoding methods typically aim at explaining behavior based on neural activity via supervised learning. However, since generally there is a strong connection between…