282 citations · 656 across the 14 of their papers we have counts for
15 papers
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
Xiao Li, Igor Gilitschenski, Guy Rosman +2
As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provi…
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…
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…
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Xin Huang, Guy Rosman, Igor Gilitschenski +4
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicti…
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…
Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
Igor Gilitschenski, Guy Rosman, Arjun Gupta +2
In this paper, we propose a novel approach for agent motion prediction in cluttered environments. One of the main challenges in predicting agent motion is accounting for location a…
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy. Our algorithm use…
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably app…
Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes
Timo Hinzmann, Thomas Stastny, Cesar Cadena +2
Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the capability to autonomously detect potential landing sites in unknown and unstructured terrain, allowing fo…
Voliro: An Omnidirectional Hexacopter With Tiltable Rotors
Mina Kamel, Sebastian Verling, Omar Elkhatib +5
Extending the maneuverability of unmanned areal vehicles promises to yield a considerable increase in the areas in which these systems can be used. Some such applications are the p…
maplab: An Open Framework for Research in Visual-inertial Mapping and Localization
Thomas Schneider, Marcin Dymczyk, Marius Fehr +4
Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and driftfree po…
Unscented Orientation Estimation Based on the Bingham Distribution
Igor Gilitschenski, Gerhard Kurz, Simon J. Julier +1
Orientation estimation for 3D objects is a common problem that is usually tackled with traditional nonlinear filtering techniques such as the extended Kalman filter (EKF) or the un…