Formal Behavioral Specification Languages and Methods for Motion Planning and Control of Autonomous Systems
Thu. Oct. 26th from 10am - 12:00pm EST
Speaker: Yash Vardhan Pant - Assistant Professor in the Department of Electrical and Computer Engineering at the University of À¶Ý®ÊÓƵ
YouTube Link to this video .
CPI Talks are free and open to everyone regardless of affiliation! High school students and non-À¶Ý®ÊÓƵ students/staff are also welcome to join.
No prior knowledge will be expected from the audience.
In this CPI Talk, Yash Vardhan Pant discussed:
Formal Behavioral Specification Languages and Methods for Motion Planning and Control of Autonomous Systems
Abstract:
Safe planning and control of single and multi-agent autonomous systems performing complex tasks has been a challenging problem. Recently, reinforcement learning-based solutions have shown great promise towards achieving these goals. However, they (usually) suffer from a lack of strong guarantees on safety, and potentially, the problem of reward hacking. In this talk, I will instead look at this problem of behavioral planning through the lens of formal behavioral specification languages, namely Signal Temporal Logic (STL) and Linear Temporal Logic (LTL), which offer a sound, succinct and unambiguous way of representing complex desired behaviors for autonomous systems. I will present a family of robust and predictive motion planning and control methods for autonomous systems with objectives defined using STL and LTL. For STL-based motion planning, we formulate efficient optimization-based methods that provide strong guarantees on performance and safety even when operating in partially known environments. For LTL-based motion planning, we present ongoing work on a model-free reinforcement learning approach that is inspired by the paradigm of self-play. The performance and scalability of the methods will be demonstrated through multi-robot simulation studies and experiments on actual quadrotor drones.
Dr. Yash Vardhan Pant is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of À¶Ý®ÊÓƵ, where he leads the Control, Learning and Logic (CL2) group. He received his PhD in Electrical Engineering from the University of Pennsylvania in 2019 and was a postdoctoral fellow at the University of California at Berkeley from 2019-2021, before joining À¶Ý®ÊÓƵ in the summer of 2021. His research focuses on decision-making for multi-agent and autonomous systems, drawing on elements of Control Theory, Machine Learning, Formal Methods and Optimization, with application to ground robots, human-robot interaction and swarms of aerial robots. More details about Dr. Pant’s research can be found at: