9 papers · 1 filter
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning
Jan Ole von Hartz, Adrian Röfer, Joschka Boedecker +1
We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation. MiDiGap enables…
Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
Aron Distelzweig, Faris Janjoš, Andreas Look +7
Recent Autonomous Driving (AD) works such as GigaFlow and PufferDrive have unlocked Reinforcement Learning (RL) at scale as a training strategy for driving policies. Yet such polic…
Latent Linear Quadratic Regulator for Robotic Control Tasks
Yuan Zhang, Shaohui Yang, Toshiyuki Ohtsuka +2
Model predictive control (MPC) has played a more crucial role in various robotic control tasks, but its high computational requirements are concerning, especially for nonlinear dyn…
MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation
Jan Ole von Hartz, Lukas Schweizer, Joschka Boedecker +1
Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency,…
Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response
Aron Distelzweig, Yiwei Wang, Faris Janjoš +5
Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense urban traffic, where lane change…
Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving
Aron Distelzweig, Faris Janjoš, Oliver Scheel +3
Traditionally, prediction and planning in autonomous driving (AD) have been treated as separate, sequential modules. Recently, there has been a growing shift towards tighter integr…