no code implementations • 19 May 2024 • Sean Vaskov, Wilko Schwarting, Chris L. Baker
In such methods, if agents are initialized in, or must visit, states where constraint violation might be inevitable, it is unclear how much they should be penalized.
no code implementations • 5 Apr 2024 • Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks.
no code implementations • 4 Jan 2024 • Rahul Ahuja, Chris Baker, Wilko Schwarting
Without relying on learning or any labeled datasets, OptFlow achieves state-of-the-art performance for scene flow estimation on popular autonomous driving benchmarks.
1 code implementation • 22 Oct 2022 • Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin Riedmiller, Daniela Rus, Markus Wulfmeier
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 associated high-dimensional action spaces.
1 code implementation • 18 May 2022 • Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents.
no code implementations • 5 Apr 2022 • Jose L. Vazquez, Alexander Liniger, Wilko Schwarting, Daniela Rus, Luc van Gool
Fundamental to the success of our method is the design of a novel multi-agent policy network that can steer a vehicle given the state of the surrounding agents and the map information.
no code implementations • 23 Nov 2021 • Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Data-driven simulators promise high data-efficiency for driving policy learning.
no code implementations • 23 Nov 2021 • Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, Daniela Rus
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
no code implementations • NeurIPS 2021 • Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, Daniela Rus
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space.
1 code implementation • 19 Feb 2021 • Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus
We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.
no code implementations • 27 Oct 2020 • Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus
Learning complex robot behaviors through interaction requires structured exploration.
no code implementations • L4DC 2020 • Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus
Deep exploration requires coordinated long-term planning.
1 code implementation • ICLR 2020 • Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
4 code implementations • NeurIPS 2020 • Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
no code implementations • 25 Sep 2019 • Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.
no code implementations • 13 Aug 2017 • 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.