29 citations · 55 across the 25 of their papers we have counts for
26 papers
Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Tai Hoang, Alessandro Trenta, Alessio Gravina +4
Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive…
TROLL: Trust Regions improve Reinforcement Learning for Large Language Models
Philipp Becker, Niklas Freymuth, Serge Thilges +2
Reinforcement Learning (RL) with PPO-like clip objectives has become the standard choice for reward-based fine-tuning of large language models (LLMs). Although recent work has expl…
AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
Niklas Freymuth, Tobias Würth, Nicolas Schreiber +9
The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve comput…
EDiT: Efficient Diffusion Transformers with Linear Compressed Attention
Philipp Becker, Abhinav Mehrotra, Ruchika Chavhan +5
Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling p…
Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects
Tai Hoang, Huy Le, Philipp Becker +2
Manipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects or cloth hanging require precis…
Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity
Emiliyan Gospodinov, Vaisakh Shaj, Philipp Becker +2
Developing foundational world models is a key research direction for embodied intelligence, with the ability to adapt to non-stationary environments being a crucial criterion. In t…
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds
Balázs Gyenes, Nikolai Franke, Philipp Becker +1
Perceiving the environment via cameras is crucial for Reinforcement Learning (RL) in robotics. While images are a convenient form of representation, they often complicate extractin…
MuTT: A Multimodal Trajectory Transformer for Robot Skills
Claudius Kienle, Benjamin Alt, Onur Celik +4
High-level robot skills represent an increasingly popular paradigm in robot programming. However, configuring the skills' parameters for a specific task remains a manual and time-c…
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Philipp Becker, Niklas Freymuth, Gerhard Neumann
Probabilistic State Space Models (SSMs) are essential for Reinforcement Learning (RL) from high-dimensional, partial information as they provide concise representations for control…
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations
Niklas Freymuth, Philipp Dahlinger, Tobias Würth +5
Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approxi…
Efficient Off-Policy Learning for High-Dimensional Action Spaces
Fabian Otto, Philipp Becker, Ngo Anh Vien +1
Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spac…
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization
Philipp Dahlinger, Philipp Becker, Maximilian Hüttenrauch +1
Stochastic gradient-based optimization is crucial to optimize neural networks. While popular approaches heuristically adapt the step size and direction by rescaling gradients, a mo…
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
Florian Seligmann, Philipp Becker, Michael Volpp +1
Bayesian deep learning (BDL) is a promising approach to achieve well-calibrated predictions on distribution-shifted data. Nevertheless, there exists no large-scale survey that eval…
Curriculum-Based Imitation of Versatile Skills
Maximilian Xiling Li, Onur Celik, Philipp Becker +3
Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelih…
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL
Philipp Becker, Sebastian Mossburger, Fabian Otto +1
Learning self-supervised representations using reconstruction or contrastive losses improves performance and sample complexity of image-based and multimodal reinforcement learning…
Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
Niklas Freymuth, Nicolas Schreiber, Philipp Becker +2
Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Thus, they can easily generalize and adapt…
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
Philipp Becker, Gerhard Neumann
Improved state space models, such as Recurrent State Space Models (RSSMs), are a key factor behind recent advances in model-based reinforcement learning (RL). Yet, despite their em…
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Vaisakh Shaj, Dieter Buchler, Rohit Sonker +2
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification. However, these models assume that the dynamic…
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
Moritz Reuss, Niels van Duijkeren, Robert Krug +3
It is well-known that inverse dynamics models can improve tracking performance in robot control. These models need to precisely capture the robot dynamics, which consist of well-un…
Specializing Versatile Skill Libraries using Local Mixture of Experts
Onur Celik, Dongzhuoran Zhou, Ge Li +2
A long-cherished vision in robotics is to equip robots with skills that match the versatility and precision of humans. For example, when playing table tennis, a robot should be cap…
Switching Recurrent Kalman Networks
Giao Nguyen-Quynh, Philipp Becker, Chen Qiu +2
Forecasting driving behavior or other sensor measurements is an essential component of autonomous driving systems. Often real-world multivariate time series data is hard to model b…
Versatile Inverse Reinforcement Learning via Cumulative Rewards
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. Current approaches usually do this w…
Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian Otto, Philipp Becker, Ngo Anh Vien +2
Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust region…
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
Vaisakh Shaj, Philipp Becker, Dieter Buchler +5
Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscl…