no code implementations • 19 Mar 2024 • Mirco Theile, Hongpeng Cao, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance.
1 code implementation • 6 Sep 2023 • Mirco Theile, Harald Bayerlein, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest.
no code implementations • 26 Jan 2023 • Mirco Theile, Daniele Bernardini, Raphael Trumpp, Cristina Piazza, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
Several machine learning (ML) applications are characterized by searching for an optimal solution to a complex task.
no code implementations • 25 Nov 2020 • Sicheng Zhao, Xuanbai Chen, Xiangyu Yue, Chuang Lin, Pengfei Xu, Ravi Krishna, Jufeng Yang, Guiguang Ding, Alberto L. Sangiovanni-Vincentelli, Kurt Keutzer
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
2 code implementations • 13 Oct 2020 • Daniel J. Fremont, Edward Kim, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
1 code implementation • 1 Sep 2020 • Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
2 code implementations • 25 Sep 2018 • Daniel J. Fremont, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
no code implementations • 31 Mar 2018 • Xiangyu Yue, Bichen Wu, Sanjit A. Seshia, Kurt Keutzer, Alberto L. Sangiovanni-Vincentelli
The framework supports data collection from both auto-driving scenes and user-configured scenes.