no code implementations • 16 Dec 2022 • Jonathan Francis, Bingqing Chen, Weiran Yao, Eric Nyberg, Jean Oh
The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the driving behaviour from experts.
1 code implementation • 18 Jul 2022 • Jingxiao Liu, Yujie Wei, Bingqing Chen
However, existing methods perform poorly when detecting small damages (e. g., cracks and exposed rebars) and thin objects with limited image samples, especially when the components of interest are highly imbalanced.
no code implementations • 5 May 2022 • Jonathan Francis, Bingqing Chen, Siddha Ganju, Sidharth Kathpal, Jyotish Poonganam, Ayush Shivani, Vrushank Vyas, Sahika Genc, Ivan Zhukov, Max Kumskoy, Anirudh Koul, Jean Oh, Eric Nyberg
In the first stage of the challenge, we evaluate an autonomous agent's ability to drive as fast as possible, while adhering to safety constraints.
no code implementations • 5 Apr 2022 • Bingqing Chen, Luca Bondi, Samarjit Das
Anomaly detection has many important applications, such as monitoring industrial equipment.
no code implementations • 14 Oct 2021 • Bingqing Chen, Jonathan Francis, Jean Oh, Eric Nyberg, Sylvia L. Herbert
Given the nature of the task, autonomous agents need to be able to 1) identify and avoid unsafe scenarios under the complex vehicle dynamics, and 2) make sub-second decision in a fast-changing environment.
1 code implementation • 19 May 2021 • Bingqing Chen, Priya Donti, Kyri Baker, J. Zico Kolter, Mario Berges
Specifically, we incorporate a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible.
1 code implementation • ICCV 2021 • James Herman, Jonathan Francis, Siddha Ganju, Bingqing Chen, Anirudh Koul, Abhinav Gupta, Alexey Skabelkin, Ivan Zhukov, Max Kumskoy, Eric Nyberg
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing.
no code implementations • 16 Dec 2020 • Henning Lange, Bingqing Chen, Mario Berges, Soummya Kar
In this paper, we show efficient strategies that circumvent this problem by differentiating through the operations of a power flow solver that embeds the power flow equations into a holomorphic function.
no code implementations • 6 Feb 2020 • Jingxiao Liu, Bingqing Chen, Siheng Chen, Mario Berges, Jacobo Bielak, HaeYoung Noh
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.