Most research in cyber-physical systems considers design of algorithms and their implementation separately. This poses a problem when dealing with cyber-physical systems with complex dynamics and uncertainty. In fact, in such cases the effectiveness of designed algorithms can be compromised by the unavoidably nonzero time needed to perform computations. The decentralization of computational resources and other requirements introduced at the implementation stage that were neglected at design will certainly negatively affect the behavior induced by the algorithm.
To properly cope with such issues, techniques for the synthesis of algorithms should incorporate information about the computations required to be performed when implemented, and, in some cases, possibly accept a degradation of performance while guaranteeing certain fundamental properties of the entire cyber-physical system, such as resilience, robustness, stability, and safety. The development of such synthesis techniques requires a radical change in the way algorithms for cyber-physical systems are designed, demanding an analysis and design framework in which, rather than being added a posteriori, computation is intrinsic in the sense that the time and cost to compute is part of the design process.
The goal of this workshop is to lay out the foundations of such framework for computation-aware algorithmic design of cyber-physical systems by bringing together experts (both practitioners and researchers) in cyber-physical systems and key areas in hardware design, real-time systems, optimization, control, safety, and verification.
The scope of the workshop includes, but is not restricted to, the following topics:
Safe Planning and Control when Autonomy is not the Only Driver
Vishnu Desaraju, Toyota Research Institute.
Abstract: When developing planning and control algorithms for autonomous systems, we often assume that these algorithms will be the only significant source of inputs to the system. However, in some applications there may be an additional "driver", external to the autonomy pipeline, that has a large impact on the performance and safety of the overall system. In this talk, I will discuss two projects that investigate different aspects of this problem.
The first considers the effects of an external driver that acts alongside the autonomy but without any shared objective. For example, the aerodynamic forces acting on a micro aerial vehicle flying through a strong wind field can dramatically alter the vehicle's motion, leading to violations of safety or operational constraints. Limited onboard computation also restricts modeling or incorporation of these nonlinear dynamics into the autonomy. This leads to the idea of Experience-driven Predictive Control (EPC). EPC builds on ideas from adaptive control and model predictive control by accumulating experience on how an external driver impacts the system while simultaneously leveraging that experience to ensure that constraints are met in a computationally efficient manner.
The second project considers the case where the external driver is actually the primary operator of the system, while the autonomy aims to assist this driver to safely achieve a common objective. A prime example of this is the next generation of advanced driver assistance systems that will be able to employ techniques developed for fully autonomous driving but in the context of keeping a human driver safe. The Toyota Guardian system is TRI's novel approach to this problem, building on ideas from a variety of domains, ranging from aircraft control to shared autonomy. I will show a few preliminary examples of how this combination of the human driver and the autonomy can achieve superhuman performance and safety.
Bio: Vishnu Desaraju is a Senior Research Scientist at the Toyota Research Institute, Ann Arbor, MI working on automated driving technologies. He received a B.S.E. in Electrical Engineering from the University of Michigan in 2008, an S.M. in Aeronautics and Astronautics from MIT in 2010, and an M.S. and Ph.D. in Robotics from Carnegie Mellon University in 2015 and 2017, respectively. He received the AIAA Guidance, Navigation, and Control Best Paper award from SciTech 2018. His research interests include developing computationally efficient motion planning and feedback control algorithms for agile autonomous systems, including autonomous cars, boats, and micro air vehicles, with a focus on mitigating the effects of uncertainty to achieve safe and reliable operation in the field.
We solicit regular papers (max 6 pages) and extended abstracts (max 2 pages). Paper submission must be performed via the EasyChair system: https://easychair.org/conferences/?conf=caadcps2021.
Regular papers must describe original work, be written and presented in English, and must not substantially overlap with papers that have been published or that are under submission. Submitted papers will be judged on the basis of significance, relevance, correctness, originality, and clarity. They should clearly identify what has been accomplished and why it is significant.
Regular paper and extended abstracts submissions should be in ACM conference template.
All accepted papers will be posted on the workshop's website and included in the ACM Digital Library.
| Submissions deadline: | March 7, 2021 |
| Notification: | March 30, 2021 |
| Final version: | April 15, 2021 |
| Workshop: | May 18, 2021 |
| 9:00-10:00 |
Vishnu Desaraju, Toyota Research Institute Invited talk: Safe Planning and Control when Autonomy is not the Only Driver Talk's Recording |
| 10:00-10:15 | Break & Social |
| 10:15-10:45 |
Mahathi Anand, Vishnu Murali, Ashutosh Trivedi and Majid Zamani Formal Verification of Hyperproperties for Control Systems Talk's Recording |
| 10:45-11:15 |
Abolfazl Lavaei, Bingzhuo Zhong, Marco Caccamo and Majid Zamani
Towards Trustworthy AI: Safe-visor Architecture for Uncertified Controllers in Stochastic Cyber-Physical Systems Talk's Recording |
| 11:15-11:30 | Break & Social |
| 11:30-12:00 |
Daniel Larsson, Dipankar Maity and Panagiotis Tsiotras Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming Talk's Recording |
| 12:00-12:30 |
Daniel Ochoa, Jorge Poveda and Cesar Uribe Computation-Aware Distributed Optimization over Networks: A Hybrid Dynamical Systems Approach Talk's Recording |
| 12:30-12:45 | Break & Social |
| 12:45-13:15 |
Usama Mehmood, Stanley Bak, Scott Smolka and Scott Stoller Safe CPS from Unsafe Controllers Talk's Recording |
| 13:15-13:45 |
Jonathan Sprinkle, Nathalie Risso, Berk Altın and Ricardo Sanfelice Challenges in Set-Valued Model-Predictive Control Talk's Recording |
| 13:45-14:00 | Break & Social |
| 14:00-14:30 |
Christopher Petersen, Sean Phillips, Dawn Hustig-Schultz and Ricardo Sanfelice Towards Hybrid Model Predictive Control for Computationally Aware Satellite Applications Talk's Recording |
| 14:30-15:00 |
Christian Llanes, Matthew Abate and Samuel Coogan Safety from in-the-loop reachability for cyber-physical systems Talk's Recording |
| 15:00-15:15 | Break & Social |
| 15:15-15:45 |
Alex Devonport and Murat Arcak Data-driven Estimation of Forward Reachable Sets Talk's Recording |
| 15:45-16:15 |
Shadi Haddad and Abhishek Halder Anytime Ellipsoidal Over-approximation of Forward Reach Sets of Uncertain Linear Systems Talk's Recording |
| 16:15-16:30 |
Break & Social & Concluding
Discussion's Recording |