29 citations · 55 across the 14 of their papers we have counts for
15 papers
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning
Sean Vaskov, Wilko Schwarting, Chris L. Baker
Reinforcement Learning (RL) for control has become increasingly popular due to its ability to learn rich feedback policies that take into account uncertainty and complex representa…
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Tim Seyde, Peter Werner, Wilko Schwarting +2
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks. The underlying coarse action s…
OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision
Rahul Ahuja, Chris Baker, Wilko Schwarting
Scene flow estimation is a crucial component in the development of autonomous driving and 3D robotics, providing valuable information for environment perception and navigation. Des…
Solving Continuous Control via Q-learning
Tim Seyde, Peter Werner, Wilko Schwarting +4
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the as…
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Ryan Sander, Wilko Schwarting, Tim Seyde +3
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et…
Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models
Jose L. Vazquez, Alexander Liniger, Wilko Schwarting +2
In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are separated, limiting the planner to react to predictions that are not informed by the planne…
Learning Interactive Driving Policies via Data-driven Simulation
Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting +3
Data-driven simulators promise high data-efficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: Small underlying d…
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski +5
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios. However, the poor photorealism and lack of d…
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Tim Seyde, Igor Gilitschenski, Wilko Schwarting +4
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known…
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski +4
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competi…
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
Tim Seyde, Wilko Schwarting, Sertac Karaman +1
Learning complex robot behaviors through interaction requires structured exploration. Planning should target interactions with the potential to optimize long-term performance, whil…
Deep Evidential Regression
Alexander Amini, Wilko Schwarting, Ava Soleimany +1
Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this…
Stochastic Dynamic Games in Belief Space
Wilko Schwarting, Alyssa Pierson, Sertac Karaman +1
Information gathering while interacting with other agents under sensing and motion uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The…
Training Support Vector Machines using Coresets
Cenk Baykal, Lucas Liebenwein, Wilko Schwarting
We present a novel coreset construction algorithm for solving classification tasks using Support Vector Machines (SVMs) in a computationally efficient manner. A coreset is a weight…
Detection of AQM on Paths using Machine Learning Methods
Cenk Baykal, Wilko Schwarting, Alex Wallar
In this paper, we address the problem of determining whether a bottleneck router on a given network path is using an AQM or a drop-tail scheme. We assume that we are given a source…