The Safe and Intelligent Autonomy Lab is part of the Ming Hsieh Department of Electrical and Computer Engineering in the USC Viterbi School of Engineering . We seek to design autonomous robotic systems that operate with guaranteed safety and performance in new settings and environments. Our research combines machine learning with classical, model-based planning and control methods in pursuit of this endeavour.
We are looking for undergraduate and graduate students to join our research group. If you are interested in robotics, control, and/or machine learning, consider joining us!
Please see this list of talks for an introduction to our areas of research.
News
[September 2023] New paper on detecting and mitigating system-level anomalies of vision-based controllers is out on arXiv.
[September 2023] New paper on Hamilton-Jacobi reachability analysis for hybrid systems with controlled and forced transitions is out on arXiv.
[August 2023] Three undergraduate students have joined our lab through the CURVE program. Welcome Kyle, Saim, and Sascha!
[May 2023] Congratulations Professor Somil Bansal, for winning the NSF CAREER Award!
[April 2023] Two new PhD students have joined our lab. Welcome Albert and Zeyuan!
[February 2023] Congratulations Kaustav, for your paper acceptance to RA-L!
[January 2023] Three papers from SIA Lab have been accepted to ICRA 2023. Congratulations, Albert, Javier, and Ken!
[January 2023] Our lab is offering a new course on learning and control for safety-critical robotic systems.
[December 2022] Javier has passed his screening exam. Congratulations, Javier!
[November 2022] New paper on finding failure cases for vision-based controller using reachability analysis is out on arXiv.
[October 2022] New paper on maintaining and updating safety in human-robot interaction scenarios, despite incorrect human prediction models.
[September 2022] New paper on providing safety guarantees for DeepReach. The scalability of DeepReach along with the proposed verification algorithm allows us to compute high-confidence safe sets for high-dimensional systems.