Murray Wiki - User contributions [en]

RMM research meetings, May 2025

2025-04-28T15:56:09Z

Jgraeben: Josefine: signed up for 5/5 3pm time slot

Please sign up for a slot below.

5 May (Mon):
* 8:30 am: Open
* 9:15 am: Open
* 3 pm: Josefine
* 3:45 pm: Open

11 May (Sun):
* 2:15 pm: Open
* 3 pm: Open
* 3:45 pm: Open
* 4:30 pm: Open

12 May (Mon):
* 10 am: Open
* 10:45 am: Open
* 3:30 pm: Open
* 4:15 pm: Open

Nok Wongpiromsarn, May 2024

2024-05-17T16:09:24Z

Jgraeben:

Nok Wongpiromsarn, an Assistant Professor at Iowa State, will visit Caltech on 20-21 May 2024.

20 May 2024
* 9:15a: Richard, 109 Steele Lab
* 10:00a: Apurva
* 10:45a: Open
* 11:30a: Noel Csomay-Shanklin (2nd floor Annenberg Lounge)
* 12p-1p: Lunch with Richard
* 1:00p: Ioannis Mandralis (CAST meeting room)
* 2:00p: Open
* 3:00p: Soon-Jo Chung (235 Guggenheim)
* 4:00p: Joel Burdick (245 Gates-Thomas)
* ~6 pm: dinner with Richard

21 May 2024
* 10:15a: Open
* 10:30a: Josefine Graebener (2nd floor Annenberg Lounge)
* 11:15a: Aaron Ames (266 Gates-Thomas)
* 12-1p: Lunch
* 1-3 pm: Apurva Badithela thesis defense
* 3:00 pm: Eric Mazumdar (meet at defense)
* 3:45 pm: Open
* 4:30 pm: Open
* 5:00 pm: Richard, 109 Steele Lab

Naira Hovakimyan, Oct 2023

2023-10-17T16:22:20Z

Jgraeben:

Naira Hovakimyan will visit CDS on 18 Oct 2023 (Wed). If you would like to meet with her, please sign up below.

* 9:15: open
* 10:00: open
* 10:45: seminar setup (Monica)
* 11:00: Seminar (location TBD)
* 12:00: Lunch - Richard, John, Eric
* 1:15: Josefine (location TBD)
* 2:00: Ersin (location TBD)
* 2:30: Max Cohen (location TBD)
* 3:00: CDS tea
* 3:45: NCS group meeting
* 5:00: depart

<hr>

<center>
Safe Learning in Autonomous Systems

'''Naira Hovakimyan''' 
UIUC
</center>

Abstract: Learning-based control paradigms have seen many success stories with various robots and co-robots in recent years. However, as these robots prepare to enter the real world, operating safely in the presence of imperfect model knowledge and external disturbances is going to be vital to ensure mission success. We introduce a class of distributionally robust adaptive control architectures that ensure robustness to distribution shifts and enable the development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications.

=== Biography ===

Naira Hovakimyan received her MS degree in Applied Mathematics from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC. She has co-authored two books, eleven patents and more than 450 refereed publications. She is the 2011 recipient of AIAA Mechanics and Control of Flight Award, the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls, and the 2019 recipient of AIAA Pendray Aerospace Literature Award. In 2014 she was awarded the Humboldt prize for her lifetime achievements. She is Fellow of AIAA and IEEE, and senior member of National Academy of Inventors. She is cofounder and chief scientist of Intelinair. Her work was featured in the New York Times, on Fox TV and CNBC.

Wen-Hua Chen, 4-21 Oct 2022

2022-10-05T19:25:38Z

Jgraeben: /* 5 Oct (Wed) */

Professor Wen-Hua Chen from the University of Loughborough will visit Caltech on 4-21 Oct 2022. A schedule for the first few days of his visit is given below. Please feel free to sign up for any open times.

{| border=1
|-
| align=top width=50% |
=== 4 Oct (Tue) ===
* 8:30 am: Richard Murray, 109 Steele Lab
* 9:00 am: Soon-Jo Chung, 235 Guggenheim (including lab tour (CAST & ARCL))
* 9:45 am: Diana Bohler (logistics)
* 10:30 am: Open
* 11:15 am: Open
* 12:00 pm: Lunch with Richard
* 1:15 pm: Houman Owhadi, 201 Steele House
* 2:00 pm: Lijun Chen, 217 Annenberg
* 2:45 pm: Andrew Taylor, 325 Annenberg
* 3:30 pm: Noel Csomay-Shanklin, 325 Annenberg
* 4:15 pm: Apurva (meet at 325 Annenberg and then walk over to 238 Annenberg or Annenberg lounge)
* 5:00 pm: Done for the day

| align=top width=50% |

=== 5 Oct (Wed) ===

* 9:00 am: Ersin Das, Location: Steele House
* 9:45 am: Anushri Dixit, Location: Steele House
* 10:30 am: Open
* 11:15 am: Prithvi Akella, Location: Steele House
* 12:00 pm: Lunch
* 1:30 pm: Anima Anandkumar, 316 Annenberg
* 2:15 pm: Josefine Graebener, Annenberg Visiting Researcher Office
* 3:00 pm: CDS Tea, Annenberg
* 3:45 pm: Seminar - 121 Annenberg
* 5:00 pm: Break
* 6:00 pm: Dinner with NCS group (meet at 109 Steele Lab)
|}

'''Stability of Optimisation-Based Control: Brief Review and New Results'''

Prof Wen-Hua Chen 
Department of Aeronautical and Automotive Engineering 
Loughborough University

With the increase of the size and the complexity of systems and their performance specifications, it is more difficult to find analytic solutions for a control system as in traditional approaches to give optimal performance. Model Predictive Control (MPC) provides a promising mechanism to realise numerical optimal solutions online to achieve best possible performance. However, establishing stability and other formal properties of this type of optimisation-based control imposes significant challenges. This talk starts with the brief overview of 30 years’ journey in developing stability theory for MPC. It points out that despite all the success, there is still a significant gap between available theoretic tools and practical applications. For example, a terminal cost that covers the optimal cost-to-go is, in general, required to add the cost function in order to ensure stability of a MPC algorithm, but most of MPC used in practical applications does not have a terminal cost (for example, all cases studies in Matlab Nonlinear MPC Toolbox do not have a terminal cost but work well). This talk presents a new approach and development in this area. The stability condition is entirely complementary to the existing terminal based MPC stability theory. Opposite to the existing MPC stability conditions, the new stability conditions cover the terminal cost being less than the optimal cost-to-go including zero terminal cost even negative. The new conditions are established based on a property of a modified stage cost. Numerical results are presented to illustrate the links and differences between the new approach and the existing stability theory. It is hoped that this work would trigger more research into understanding the interaction between optimisation and feedback loops in both the AI and the control community so ensure efficiency and safety of future robotics and autonomous systems.

Dr Wen-Hua Chen holds Professor in Autonomous Vehicles in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK. Prof. Chen has a considerable experience in control, signal processing and artificial intelligence and their applications in aerospace, automotive and agriculture systems. In the last 15 years, he has been working on the development and application of unmanned aircraft system and intelligent vehicle technologies, spanning autopilots, situational awareness, decision making, verification, remote sensing for precision agriculture and environment monitoring. He is a Chartered Engineer, and a Fellow of IEEE, the Institution of Mechanical Engineers and the Institution of Engineering and Technology, UK. Recently Prof Chen was awarded an EPSRC (Engineering and Physical Science Research Council) Established Career Fellowship in developing control theory for next generation of control systems to enable high levels of automation such as robotics and autonomous systems.

Wen-Hua Chen, 4-21 Oct 2022

2022-10-01T19:24:08Z

Jgraeben: /* 5 Oct (Wed) */

Professor Wen-Hua Chen from the University of Loughborough will visit Caltech on 4-21 Oct 2022. A schedule for the first few days of his visit is given below. Please feel free to sign up for any open times.

{| border=1
|-
| align=top width=50% |
=== 4 Oct (Tue) ===
* 8:30 am: Richard Murray, 109 Steele Lab
* 9:00 am: Soon-Jo Chung, 235 Guggenheim (including lab tour (CAST & ARCL))
* 9:45 am: Open
* 10:30 am: Open
* 11:15 am: Open
* 12:00 pm: Lunch with Richard
* 1:15 pm: Houman Owhadi, 201 Steele House
* 2:00 pm: Open
* 2:45 pm: Open
* 3:30 pm: Open
* 4:15 pm: Open
* 5:00 pm: Done for the day

| align=top width=50% |

=== 5 Oct (Wed) ===

* 9:00 am: Open
* 9:45 am: Open
* 10:30 am: Open
* 11:15 am: Prithvi Akella, (Place TBD)
* 12:00 pm: Lunch
* 1:30 pm: Anima Anandkumar, 316 Annenberg
* 2:15 pm: Josefine Graebener, Location TBD
* 3:00 pm: CDS Tea, Annenberg
* 3:45 pm: Seminar - 121 Annenberg
* 5:00 pm: Richard Murray, 109 Steele Lab
* 6:00 pm: Dinner with NCS group
|}

'''Stability of Optimisation-Based Control: Brief Review and New Results'''

Prof Wen-Hua Chen 
Department of Aeronautical and Automotive Engineering 
Loughborough University

With the increase of the size and the complexity of systems and their performance specifications, it is more difficult to find analytic solutions for a control system as in traditional approaches to give optimal performance. Model Predictive Control (MPC) provides a promising mechanism to realise numerical optimal solutions online to achieve best possible performance. However, establishing stability and other formal properties of this type of optimisation-based control imposes significant challenges. This talk starts with the brief overview of 30 years’ journey in developing stability theory for MPC. It points out that despite all the success, there is still a significant gap between available theoretic tools and practical applications. For example, a terminal cost that covers the optimal cost-to-go is, in general, required to add the cost function in order to ensure stability of a MPC algorithm, but most of MPC used in practical applications does not have a terminal cost (for example, all cases studies in Matlab Nonlinear MPC Toolbox do not have a terminal cost but work well). This talk presents a new approach and development in this area. The stability condition is entirely complementary to the existing terminal based MPC stability theory. Opposite to the existing MPC stability conditions, the new stability conditions cover the terminal cost being less than the optimal cost-to-go including zero terminal cost even negative. The new conditions are established based on a property of a modified stage cost. Numerical results are presented to illustrate the links and differences between the new approach and the existing stability theory. It is hoped that this work would trigger more research into understanding the interaction between optimisation and feedback loops in both the AI and the control community so ensure efficiency and safety of future robotics and autonomous systems.

Dr Wen-Hua Chen holds Professor in Autonomous Vehicles in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK. Prof. Chen has a considerable experience in control, signal processing and artificial intelligence and their applications in aerospace, automotive and agriculture systems. In the last 15 years, he has been working on the development and application of unmanned aircraft system and intelligent vehicle technologies, spanning autopilots, situational awareness, decision making, verification, remote sensing for precision agriculture and environment monitoring. He is a Chartered Engineer, and a Fellow of IEEE, the Institution of Mechanical Engineers and the Institution of Engineering and Technology, UK. Recently Prof Chen was awarded an EPSRC (Engineering and Physical Science Research Council) Established Career Fellowship in developing control theory for next generation of control systems to enable high levels of automation such as robotics and autonomous systems.

SURF 2022: Specification Monitor for Testing of Autonomous Systems

2021-12-20T15:54:39Z

Jgraeben: /* Project Description */

2022 SURF: Project Description

- Mentor: Richard Murray

- Co-mentor: Josefine Graebener

==Project Description==

[[File:Duckiebot_db21.jpg|thumb|500px|right|Duckiebot model DB21. Image from https://www.duckietown.org/mooc]]

Testing of autonomous vehicles (AVs) is a very time and cost intensive effort, which needs to be repeated after every system modification [1]. Thus finding a way to improve the efficiency of testing is a very valuable step on the path to more autonomy. We propose a framework which `merges' multiple unit tests into one fewer tests, which guarantee to cover what is tested in the unit tests.

This framework uses a model of the system to find the merged test via a simulation and tree search, this model is non-deterministic, but expected to be perfect. But realistically , this system model will not cover the entire system in all possible situations in the real world -- due to the gap between simulation and real world -- therefore the execution of the test could not result in the desired outcome when it is run on the actual hardware. While executing the testing campaign, we need to find a way to automatically evaluate the tests --- whether it satisfied the test specification -- for example testing a left turn -- and whether the system behaved as expected -- for example safe and comfortable driving -- and then learn from the test outcomes to improve the future testing campaign.

The summer project will be implementing a `monitor', which visualizes whether the actual test fulfilled the desired outcome and implement it on the Duckietown hardware [2]. The test monitor needs to show the satisfaction or violation of the system specification and the test specification. This test monitor will enable learning from previously run tests and improve the testing suite by modifying the following tests in case the hardware did not perform as expected in the test. After completing the monitor, the output can be used to generate an improved testing campaign and determine if improvements to the testing campaign could be made.

Familiarity with robotic hardware (we are using Duckiebots DB21), Python 3, ROS, and Docker would be beneficial.

==References==
[1] Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?. Transportation Research Part A: Policy and Practice, 94, 182-193.

[2] Paull, L., Tani, J., Ahn, H., Alonso-Mora, J., Carlone, L., Cap, M., ... & Censi, A. (2017, May). Duckietown: an open, inexpensive and flexible platform for autonomy education and research. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1497-1504). IEEE.

SURF 2022: Specification Monitor for Testing of Autonomous Systems

2021-12-20T15:48:34Z

Jgraeben: /* Project Description */

2022 SURF: Project Description

- Mentor: Richard Murray

- Co-mentor: Josefine Graebener

==Project Description==

[[File:Duckiebot_db21.jpg|thumb|500px|right|Duckiebot model DB21. Image from https://www.duckietown.org/mooc]]

Testing of autonomous vehicles (AVs) is a very time and cost intensive effort, which needs to be repeated after every system modification [1]. Thus finding a way to improve the efficiency of testing is a very valuable step on the path to more autonomy. We propose a framework which `merges' multiple unit tests into one single test, which guarantees to cover what is be tested in the unit tests. This framework uses a model of the system to find the merged test, which is expected to be perfect. This system model will not cover the entire system in all possible situations in the real world, therefore the test could not result in the desired outcome when it is run on the actual hardware. While executing the testing campaign, we need to find a way to automatically evaluate the tests --- whether it satisfied the test specification and whether the system behaved as expected --- and learn from the test outcomes to improve the testing campaign.

The summer project will be implementing a `monitor', which visualizes whether the actual test fulfilled the desired outcome and implement it on the Duckietown hardware [2]. The test monitor needs to show the satisfaction or violation of the system specification and the test specification. This test monitor will enable learning from previously run tests and improve the testing suite by modifying the following tests in case the hardware did not perform as expected in the test. After completing the monitor, the output can be used to generate an improved testing campaign and determine if improvements to the previous approach could be made.

Familiarity with robotic hardware, Python 3, ROS, and Docker would be beneficial.

==References==
[1] Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?. Transportation Research Part A: Policy and Practice, 94, 182-193.

[2] Paull, L., Tani, J., Ahn, H., Alonso-Mora, J., Carlone, L., Cap, M., ... & Censi, A. (2017, May). Duckietown: an open, inexpensive and flexible platform for autonomy education and research. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1497-1504). IEEE.

File:Duckiebot db21.jpg

2021-12-20T15:46:19Z

Jgraeben: from https://www.duckietown.org/mooc

== Summary ==
from https://www.duckietown.org/mooc

SURF 2022: Specification Monitor for Testing of Autonomous Systems

2021-12-18T23:52:19Z

Jgraeben: Created page with "2022 SURF: Project Description - Mentor: Richard Murray - Co-mentor: Josefine Graebener ==Project Description== Testing of autonomous vehicles (AVs) is a very time and c..."

2022 SURF: Project Description

- Mentor: Richard Murray

- Co-mentor: Josefine Graebener

==Project Description==

Testing of autonomous vehicles (AVs) is a very time and cost intensive effort, which needs to be repeated after every system modification [1]. Thus finding a way to improve the efficiency of testing is a very valuable step on the path to more autonomy. We propose a framework which `merges' multiple unit tests into one single test, which guarantees to cover what is be tested in the unit tests. This framework uses a model of the system to find the merged test, which is expected to be perfect. This system model will not cover the entire system in all possible situations in the real world, therefore the test could not result in the desired outcome when it is run on the actual hardware. While executing the testing campaign, we need to find a way to automatically evaluate the tests --- whether it satisfied the test specification and whether the system behaved as expected --- and learn from the test outcomes to improve the testing campaign.

The summer project will be implementing a `monitor', which visualizes whether the actual test fulfilled the desired outcome and implement it on the Duckietown hardware [2]. The test monitor needs to show the satisfaction or violation of the system specification and the test specification. This test monitor will enable learning from previously run tests and improve the testing suite by modifying the following tests in case the hardware did not perform as expected in the test. After completing the monitor, the output can be used to generate an improved testing campaign and determine if improvements to the previous approach could be made.

Familiarity with robotic hardware, Python 3, ROS, and Docker would be beneficial.

==References==
[1] Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?. Transportation Research Part A: Policy and Practice, 94, 182-193.

[2] Paull, L., Tani, J., Ahn, H., Alonso-Mora, J., Carlone, L., Cap, M., ... & Censi, A. (2017, May). Duckietown: an open, inexpensive and flexible platform for autonomy education and research. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1497-1504). IEEE.

SURF discussions, Feb 2021

2021-01-28T09:37:08Z

Jgraeben: /* 2 Feb (Tue) */

Slots for talking with applicants and co-mentors about SURF projects. Please sign up for one of the slots below. All times are PST. __NOTOC__

In preparation for our conversation, please do the following:
* SURF students should work with their co-mentors to find a time the meeting/Skype call. (For Skype calls, co-mentors should initiate.)
* Please make sure you have read the material in the description of your project, so that you are prepared to talk about what the project is about and we can narrow in on the key ideas that will be the basis of your proposal
* Please take a look at the [[SURF GOTChA chart]] page, which is the format that we will use for the first iteration of your project proposal.

{| border=1 width=100%
|- valign=top
| width=25% |
==== 1 Feb (Mon) ====
* 5:00 pm PST: open
* 5:30 pm PST: open
| width=25% |

==== 2 Feb (Tue) ====
* 4:00 pm PST: Christian/Josefine
* 4:30 pm PST: open
| width=25% |

==== 3 Feb (Wed) ====
* 9:00 am PST: open
* 9:30 am PST: open

|}

The agenda for the phone call is (roughly):

# Description of the basic idea behind the project (based on applicant's understanding)
# Discussion about approaches, things to read, variations to consider, etc
# Discussion of the format of the proposal
# Questions and discussion about the process

SURF 2021: Test Design for Extremely Resilient System

2021-01-04T12:33:22Z

Jgraeben: /* Project Description */

'''[[SURF 2021|2021 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==
[[File:Experiment prop.png|thumb|500px|right|Experiment proposition for the extreme resilience project. The rover is located in a test area with several target locations of different scientific value. The goal is to maximize the scientific output of the mission (maximize the reward of the locations visited and downlinked), while components of the rover fail and therefore its capabilities degrade. For every failure in the system, the rover will need to autonomously decide on the best action to take to maximize the reward of the mission.]]

As future space missions become more advanced, targets further away from Earth become reachable, and with them come greater technical challenges.
Spacecraft will have to downlink valuable science data, while potentially encountering more uncertain, hostile and isolated environments. Increasing distance from Earth results in a substantial light time delay, while hostile environments take a toll on the spacecraft and limit the mission lifetime. Due to these reasons, time is a critical resource, and Earth-based problem resolution may be inadequate – the spacecraft needs to resolve problems autonomously and efficiently. It will need to be resilient in this uncertain and communication-constrained environment [1].

Functional redundancy enables a system to continue operation by leveraging incidental capabilities of components, such as using a CPU to generate heat or a wheel as a rotation sensor, and therefore gain resilience beyond component redundancy and diversification.
We are developing an “extreme resilience” concept for space missions which aims to utilize functional redundancy in spacecraft components to autonomously reconfigure the spacecraft to adapt to spacecraft failures or environmental changes. This adaptation will be made via on-board reasoning to decide which action will lead towards providing the most valuable science data.

As a motivating application, we chose to develop a concept of a rover whose mission is to reach one particular location, or to get there as close as possible. The spacecraft should redistribute computation tasks between the onboard and the payload computer, reroute commands within the spacecraft and continue driving while part of the system fails.

Testing of this system should be done according to the principles of chaos engineering [2], a software testing practice invented by Netflix, which defines a way of independent testing during production, by breaking the system to gain confidence in its capabilities.

This SURF project should test the extreme resilience concept by defining a metric to evaluate the system’s resilience, a hypothesis of the expected system behavior, and design test cases subject to the constraints given. For example, fail 5 components in any order for an arbitrary amount of time, and try to do the most damage to the system. These test cases shall then be implemented and analyzed according to the metric to identify weaknesses of the system. This can be done entirely via simulation or can be implemented on a hardware implementation, depending on what is available and possible to use at that point.

Familiarity with Python would be beneficial. If a hardware implementation is chosen, experience in experimental robotics and ROS is an advantage.

==References==

[1] 2018 Workshop on Autonomy for Future NASA Science Missions: Ocean Worlds Design Reference Mission Reports, https://science.nasa.gov/technology/2018-autonomy-workshop/, October 10-11, 2018, Pittsburgh, PA

[2] Ali Basiri, Niosha Behnam, Ruud de Rooij, Lorin Hochstein, Luke Kosewski, Justin Reynolds, Casey Rosenthal, "Chaos Engineering", IEEE Software, vol.33, no. 3, pp. 3541, MayJune 2016, DOI:10.1109/MS.2016.60

SURF 2021: Test Design for Extremely Resilient System

2021-01-04T12:32:58Z

Jgraeben: /* Project Description */

'''[[SURF 2021|2021 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==
[[File:Experiment prop.png|thumb|500px|right|Experiment proposition for the extreme resilience project. The rover is located in a test area with several target locations of different scientific value. The goal is to maximize the scientific output of the mission (maximize the reward of the locations visited and downlinked), while components of the rover fail and therefore its capabilities degrade. For every failure in the system, the rover will need to autonomously decide on the best action to take to maximize the reward of the mission.]]

As future space missions become more advanced, targets further away from Earth become reachable, and with them come greater technical challenges.
Spacecraft will have to downlink valuable science data, while potentially encountering more uncertain, hostile and isolated environments. Increasing distance from Earth results in a substantial light time delay, while hostile environments take a toll on the spacecraft and limit the mission lifetime. Due to these reasons, time is a critical resource, and Earth-based problem resolution may be inadequate – the spacecraft needs to resolve problems autonomously and efficiently. It will need to be resilient in this uncertain and communication-constrained environment [1].

Functional redundancy enables a system to continue operation by leveraging incidental capabilities of components, such as using a CPU to generate heat or a wheel as a rotation sensor, and therefore gain resilience beyond component redundancy and diversification.
We are developing an “extreme resilience” concept for space missions which aims to utilize functional redundancy in spacecraft components to autonomously reconfigure the spacecraft to adapt to spacecraft failures or environmental changes. This adaptation will be made via on-board reasoning to decide which action will lead towards providing the most valuable science data.

As a motivating application, we chose to develop a concept of a rover whose mission is to reach one particular location, or to get there as close as possible. The spacecraft should redistribute computation tasks between the onboard and the payload computer, reroute commands within the spacecraft and continue driving while part of the system fails.

Testing of this system should be done according to the principles of chaos engineering [2], a software testing practice invented by Netflix, which defines a way of independent testing during production, by breaking the system to gain confidence in its capabilities.

This SURF project should test the extreme resilience concept by defining a metric to evaluate the system’s resilience, a hypothesis of the expected system behavior, and design test cases subject to the constraints given. For example, fail 5 components in any order for an arbitrary amount of time, and try to do the most damage to the system. These test cases shall then be implemented and analyzed according to the metric to identify weaknesses of the system. This can be done entirely via simulation or can be implemented on a hardware implementation, depending on what is available and possible to use at that point.
Familiarity with Python would be beneficial. If a hardware implementation is chosen, experience in experimental robotics and ROS is an advantage.

==References==

[1] 2018 Workshop on Autonomy for Future NASA Science Missions: Ocean Worlds Design Reference Mission Reports, https://science.nasa.gov/technology/2018-autonomy-workshop/, October 10-11, 2018, Pittsburgh, PA

[2] Ali Basiri, Niosha Behnam, Ruud de Rooij, Lorin Hochstein, Luke Kosewski, Justin Reynolds, Casey Rosenthal, "Chaos Engineering", IEEE Software, vol.33, no. 3, pp. 3541, MayJune 2016, DOI:10.1109/MS.2016.60

SURF 2021: Test Design for Extremely Resilient System

2021-01-04T12:30:39Z

Jgraeben: /* Project Description */

'''[[SURF 2021|2021 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==

As future space missions become more advanced, targets further away from Earth become reachable, and with them come greater technical challenges.
Spacecraft will have to downlink valuable science data, while potentially encountering more uncertain, hostile and isolated environments. Increasing distance from Earth results in a substantial light time delay, while hostile environments take a toll on the spacecraft and limit the mission lifetime. Due to these reasons, time is a critical resource, and Earth-based problem resolution may be inadequate – the spacecraft needs to resolve problems autonomously and efficiently. It will need to be resilient in this uncertain and communication-constrained environment [1].

Functional redundancy enables a system to continue operation by leveraging incidental capabilities of components, such as using a CPU to generate heat or a wheel as a rotation sensor, and therefore gain resilience beyond component redundancy and diversification.
We are developing an “extreme resilience” concept for space missions which aims to utilize functional redundancy in spacecraft components to autonomously reconfigure the spacecraft to adapt to spacecraft failures or environmental changes. This adaptation will be made via on-board reasoning to decide which action will lead towards providing the most valuable science data.

As a motivating application, we chose to develop a concept of a rover whose mission is to reach one particular location, or to get there as close as possible. The spacecraft should redistribute computation tasks between the onboard and the payload computer, reroute commands within the spacecraft and continue driving while part of the system fails.

Testing of this system should be done according to the principles of chaos engineering [2], a software testing practice invented by Netflix, which defines a way of independent testing during production, by breaking the system to gain confidence in its capabilities.

This SURF project should test the extreme resilience concept by defining a metric to evaluate the system’s resilience, a hypothesis of the expected system behavior, and design test cases subject to the constraints given. For example, fail 5 components in any order for an arbitrary amount of time, and try to do the most damage to the system. These test cases shall then be implemented and analyzed according to the metric to identify weaknesses of the system. This can be done entirely via simulation or can be implemented on a hardware implementation, depending on what is available and possible to use at that point.
Familiarity with Python would be beneficial. If a hardware implementation is chosen, experience in experimental robotics and ROS is an advantage.

[[File:Experiment prop.png|thumb|500px|right|Experiment proposition for the extreme resilience project. The rover is located in a test area with several target locations of different scientific value. The goal is to maximize the scientific output of the mission (maximize the reward of the locations visited and downlinked), while components of the rover fail and therefore its capabilities degrade. For every failure in the system, the rover will need to autonomously decide on the best action to take to maximize the reward of the mission.]]

==References==

[1] 2018 Workshop on Autonomy for Future NASA Science Missions: Ocean Worlds Design Reference Mission Reports, https://science.nasa.gov/technology/2018-autonomy-workshop/, October 10-11, 2018, Pittsburgh, PA

[2] Ali Basiri, Niosha Behnam, Ruud de Rooij, Lorin Hochstein, Luke Kosewski, Justin Reynolds, Casey Rosenthal, "Chaos Engineering", IEEE Software, vol.33, no. 3, pp. 3541, MayJune 2016, DOI:10.1109/MS.2016.60

File:Experiment prop.png

2021-01-04T12:25:29Z

Jgraeben: Extreme resilience project experiment proposition.

Extreme resilience project experiment proposition.

SURF 2021: Test Design for Extremely Resilient System

2021-01-04T12:23:46Z

Jgraeben: /* Project Description */

'''[[SURF 2021|2021 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==

As future space missions become more advanced, targets further away from Earth become reachable, and with them come greater technical challenges.
Spacecraft will have to downlink valuable science data, while potentially encountering more uncertain, hostile and isolated environments. Increasing distance from Earth results in a substantial light time delay, while hostile environments take a toll on the spacecraft and limit the mission lifetime. Due to these reasons, time is a critical resource, and Earth-based problem resolution may be inadequate – the spacecraft needs to resolve problems autonomously and efficiently. It will need to be resilient in this uncertain and communication-constrained environment [1].

Functional redundancy enables a system to continue operation by leveraging incidental capabilities of components, such as using a CPU to generate heat or a wheel as a rotation sensor, and therefore gain resilience beyond component redundancy and diversification.
We are developing an “extreme resilience” concept for space missions which aims to utilize functional redundancy in spacecraft components to autonomously reconfigure the spacecraft to adapt to spacecraft failures or environmental changes. This adaptation will be made via on-board reasoning to decide which action will lead towards providing the most valuable science data.

As a motivating application, we chose to develop a concept of a rover whose mission is to reach one particular location, or to get there as close as possible. The spacecraft should redistribute computation tasks between the onboard and the payload computer, reroute commands within the spacecraft and continue driving while part of the system fails.

Testing of this system should be done according to the principles of chaos engineering [2], a software testing practice invented by Netflix, which defines a way of independent testing during production, by breaking the system to gain confidence in its capabilities.

This SURF project should test the extreme resilience concept by defining a metric to evaluate the system’s resilience, a hypothesis of the expected system behavior, and design test cases subject to the constraints given. For example, fail 5 components in any order for an arbitrary amount of time, and try to do the most damage to the system. These test cases shall then be implemented and analyzed according to the metric to identify weaknesses of the system. This can be done entirely via simulation or can be implemented on a hardware implementation, depending on what is available and possible to use at that point.
Familiarity with Python would be beneficial. If a hardware implementation is chosen, experience in experimental robotics and ROS is an advantage.

==References==

[1] 2018 Workshop on Autonomy for Future NASA Science Missions: Ocean Worlds Design Reference Mission Reports, https://science.nasa.gov/technology/2018-autonomy-workshop/, October 10-11, 2018, Pittsburgh, PA

[2] Ali Basiri, Niosha Behnam, Ruud de Rooij, Lorin Hochstein, Luke Kosewski, Justin Reynolds, Casey Rosenthal, "Chaos Engineering", IEEE Software, vol.33, no. 3, pp. 3541, MayJune 2016, DOI:10.1109/MS.2016.60

SURF 2021: Test Design for Extremely Resilient System

2020-12-16T22:10:06Z

Jgraeben: Created page with "'''2021 SURF: project description''' * Mentor: Richard M. Murray * Co-mentor: Josefine Graebener ==Project Description== As future space missions become more..."

'''[[SURF 2021|2021 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==

As future space missions become more advanced, targets further away from Earth become reachable, and with them come greater technical challenges.
Spacecraft will have to downlink valuable science data, while potentially encountering more uncertain, hostile and isolated environments. Increasing distance from Earth results in a substantial light time delay, while hostile environments take a toll on the spacecraft and limit the mission lifetime. Due to these reasons, time is a critical resource, and Earth-based problem resolution may be inadequate – the spacecraft needs to resolve problems autonomously and efficiently. It will need to be resilient in this uncertain and communication-constrained environment [1].

Functional redundancy enables a system to continue operation by leveraging incidental capabilities of components, such as using a CPU to generate heat or a wheel as a rotation sensor, and therefore gain resilience beyond component redundancy and diversification.
We are developing an “extreme resilience” concept for space missions which aims to utilize functional redundancy in spacecraft components to autonomously reconfigure the spacecraft to adapt to spacecraft failures or environmental changes. This adaptation will be made via on-board reasoning to decide which action will lead towards providing the most valuable science data.

As a motivating application, we chose to develop a concept of a rover whose mission is to reach one particular location, or to get there as close as possible. The spacecraft should redistribute computation tasks between the onboard and the payload computer, reroute commands within the spacecraft and continue driving while part of the system fails.

Testing of this system should be done according to the principles of chaos engineering [2], a software testing practice invented by Netflix, which defines a way of independent testing during production, by breaking the system to gain confidence in its capabilities.

This SURF project should test the extreme resilience concept by defining a metric to evaluate the system’s resilience, a hypothesis of the expected system behavior, and design test cases subject to the constraints given. For example, fail 5 components in any order for an arbitrary amount of time, and try to do the most damage to the system. These test cases shall then be implemented and analyzed according to the metric to identify weaknesses of the system. This can be done entirely via simulation or can be implemented on a hardware implementation, depending on what is available and possible to use at that point.

==References==

[1] 2018 Workshop on Autonomy for Future NASA Science Missions: Ocean Worlds Design Reference Mission Reports, https://science.nasa.gov/technology/2018-autonomy-workshop/, October 10-11, 2018, Pittsburgh, PA

[2] Ali Basiri, Niosha Behnam, Ruud de Rooij, Lorin Hochstein, Luke Kosewski, Justin Reynolds, Casey Rosenthal, "Chaos Engineering", IEEE Software, vol.33, no. 3, pp. 3541, MayJune 2016, DOI:10.1109/MS.2016.60

Liren Yang, 5-6 Feb 2020

2020-02-03T17:15:30Z

Jgraeben: /* Schedule */

Liren Yang is a PhD student at U. Michigan who has recently defended his thesis on correct-by-construction fault-tolerant control synthesis. He will visit Caltech on 5-6 Feb. Sign up here if you would lik to meet with him (use your Caltech credentials).

=== Schedule ===

5 Feb (Wed)
* 2:00p: NCS group meeting
* 3:00p: CDS tea
* 3:30p: Chuchu
* 4:15p: Karena
* 5:00p: Informal seminar - 121 Annenberg
* 6:00p: Dinner with Richard + students (add your name here if you want to go): Apurva

6 Feb (Thu)
* 9:15a: Richard Murray, 109 Steele Lab
* 10:00a: Apurva, Pick up from Richard’s office. Meeting location: CDS Library
* 10:45a: Yuxiao
* 11:30a: Sumanth
* 12:15p: Lunch
* 1:15p: Open
* 2:00p: Open
* 2:45p: Josefine
* 3:30p: Done for the day

=== Talk ===

Correct-by-construction fault-tolerant control of complex dynamical systems

Correct-by-construction control synthesis methods can be used to algorithmically design controllers that render dynamical systems to satisfy complex tasks specified by formal languages. However, there is a gap between such techniques and real applications, especially when the systems experience faults, such as physical component failures and extreme operating conditions. Moreover, it is conservative and computationally expensive to apply such techniques developed for pure discrete systems directly to continuous-state dynamical systems. In this talk, I will present our work tackling these challenges. First, I will briefly mention our work on guaranteed fault-detection with linear temporal logic (LTL) constraints, and a hierarchical abstraction-based fault-tolerant controller synthesis approach. Then, regarding the first issue, I will focus on how to construct abstractions for systems with complex but structured dynamics. In particular, we leverage a special system structural property called mixed monotonicity to ease abstraction computation, and to develop synthesis techniques to incorporate this property. The presented methodology will be illustrated on a fuel cell thermal-power management problem.

SURF discussions, Jan 2020

2020-01-23T00:14:52Z

Jgraeben: /* 30 Jan (Thu) */

Slots for talking with applicants and co-mentors about SURF projects. Please sign up for one of the slots below. All times are PST. __NOTOC__

In preparation for our conversation, please do the following:
* SURF students should work with their co-mentors to find a time the meeting/Skype call. (For Skype calls, co-mentors should initiate.)
* Please make sure you have read the material in the description of your project, so that you are prepared to talk about what the project is about and we can narrow in on the key ideas that will be the basis of your proposal
* Please take a look at the [[SURF GOTChA chart]] page, which is the format that we will use for the first iteration of your project proposal.
* Please read through the [[http:sfp.caltech.edu/students/proposal/surf_and_amgen_proposals|SURF proposal information page]] to see what the SURF office requires (and when)

{| border=1 width=100%
|- valign=top
| width=25% |
==== 24 Jan (Fri) ====
* 2:00 pm PST: open
* 2:30 pm PST: open
<hr>
* 4:30 pm PST: open
* 5:00 pm PST: open
| width=25% |

==== 28 Jan (Tue) ====
* 1:30 pm PST: open
<hr>
* 5:00 pm PST: open
* 5:30 pm PST: Ivy and Apurva
| width=25% |

==== 30 Jan (Thu) ====
* 9:00 am PST: open
* 9:30 am PST: Tom and Josefine
| width=25% |

==== 3 Feb (Mon, if needed) ====
* 9:00 am PST: Open
* 9:30 am PST: Chelsea, Katherine
<hr>
* 5:00 pm PST: Open

|}

The agenda for the phone call is (roughly):

# Description of the basic idea behind the project (based on applicant's understanding)
# Discussion about approaches, things to read, variations to consider, etc
# Discussion of the format of the proposal
# Questions and discussion about the process

SURF 2020: Hardware Implementation of Contract-Based Design for Automated Valet Parking System

2019-12-30T18:08:56Z

Jgraeben: /* References */

'''[[SURF 2020|2020 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==
Due to the increasing complexity of cyber-physical systems, a modular approach to the design of these systems is necessary, while also guaranteeing correct and reliable behavior. Hybrid systems with multiple layers of abstraction need to ensure the correct implementation and composition of the components. For example a system can consist of a high level decision making process, which is then refined to lower layers of abstraction, which implement the desired behavior by composing components on each layer. For this layered design to satisfy the specification, rules for the composition of the components and refinement of the layers are necessary. This is guaranteed by contracts between the layers and the components in each layer of the system, which define the accepted behavior of the overall system [5].
A system specification can be defined in terms of contracts using temporal logic as specification language, which can be formally verified [3]. Systems can be designed according to a contract-based methodology as proposed in [1]. This defines the system under normal operations, but for safety-critical systems such as self driving cars it is necessary to guarantee a specific behavior in the presence of failures as well. To achieve this we introduce reactivity into our contract design, which defines how the system will react to failure scenarios [2][4].

The example under consideration is an automatic valet parking, which shall be verified in an experimental setup. The focus of this project is the vertical contract architecture (contracts between the layers) which define the refinement of the specification including pre-defined failure scenarios.

The study objective is to set up the parking lot infrastructure in a small scale model using multiple R/C cars and verify the control strategy, which means executing the parking and retrieving process with multiple cars in the parking lot during normal operations and including failure scenarios, such as cars stopping during the process or unexpected obstacles.

The project requires some familiarity with Python or enough programming experience to learn it in a short time. Experience in experimental robotics and ROS is an advantage.

[[File:AVP.png|thumb|500px|right|Parking lot layout.]]

==References==

[1] Albert Benveniste, Benoit Caillaud, Dejan Nickovic, Roberto Passerone, Jean-Baptiste Raclet, et al.. Contracts for System Design. [Research Report] RR-8147, INRIA. 2012, pp.65. 〈hal-00757488〉

[2] Burdick J. W. et al., Sensing, Navigation and Reasoning Technologies for the DARPA Urban Challenge, DARPA Urban Challenge Final Report, 2007

[3] P. Nuzzo, H. Xu, N. Ozay, J. B. Finn, A. L. Sangiovanni-Vincentelli, R. M. Murray, A. Donze, S. A. Seshia. A Contract-Based Methodology for Aircraft Electric Power System Design. IEEE Access, 2014. DOI 10.1109/ACCESS.2013.2295764

[4] T. Phan-Minh, R. M. Murray, Contracts of Rectivity, Submitted, Int'l Conf on Formal Modeling and Analysis of Timed Systems (FORMATS) 2019

[5] Alberto Sangiovanni-Vincentelli, Werner Damm, Roberto Passerone, Taming Dr. Frankenstein: Contract-Based Design for Cyber-Physical Systems, European Journal of Control (2012)3:217–238

SURF 2020: Hardware Implementation of Contract-Based Design for Automated Valet Parking System

2019-12-30T18:06:31Z

Jgraeben: /* References */

'''[[SURF 2020|2020 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==
Due to the increasing complexity of cyber-physical systems, a modular approach to the design of these systems is necessary, while also guaranteeing correct and reliable behavior. Hybrid systems with multiple layers of abstraction need to ensure the correct implementation and composition of the components. For example a system can consist of a high level decision making process, which is then refined to lower layers of abstraction, which implement the desired behavior by composing components on each layer. For this layered design to satisfy the specification, rules for the composition of the components and refinement of the layers are necessary. This is guaranteed by contracts between the layers and the components in each layer of the system, which define the accepted behavior of the overall system [5].
A system specification can be defined in terms of contracts using temporal logic as specification language, which can be formally verified [3]. Systems can be designed according to a contract-based methodology as proposed in [1]. This defines the system under normal operations, but for safety-critical systems such as self driving cars it is necessary to guarantee a specific behavior in the presence of failures as well. To achieve this we introduce reactivity into our contract design, which defines how the system will react to failure scenarios [2][4].

The example under consideration is an automatic valet parking, which shall be verified in an experimental setup. The focus of this project is the vertical contract architecture (contracts between the layers) which define the refinement of the specification including pre-defined failure scenarios.

The study objective is to set up the parking lot infrastructure in a small scale model using multiple R/C cars and verify the control strategy, which means executing the parking and retrieving process with multiple cars in the parking lot during normal operations and including failure scenarios, such as cars stopping during the process or unexpected obstacles.

The project requires some familiarity with Python or enough programming experience to learn it in a short time. Experience in experimental robotics and ROS is an advantage.

[[File:AVP.png|thumb|500px|right|Parking lot layout.]]

==References==

[1] Albert Benveniste, Benoit Caillaud, Dejan Nickovic, Roberto Passerone, Jean-Baptiste Raclet, et al.. Contracts for System Design. [Research Report] RR-8147, INRIA. 2012, pp.65. 〈hal-00757488〉

[2] Burdick J. W. et al., Sensing, Navigation and Reasoning Technologies for the DARPA Urban Challenge, DARPA Urban Challenge Final Report, 2007

[3] P. Nuzzo, H. Xu, N. Ozay, J. B. Finn, A. L. Sangiovanni-Vincentelli, R. M. Murray, A. Donze, S. A. Seshia. A Contract-Based Methodology for Aircraft Electric Power System Design. IEEE Access, 2014. DOI 10.1109/ACCESS.2013.2295764

[4] Phan-Minh, T., Murray, R., Contracts of Rectivity, Submitted, Int'l Conf on Formal Modeling and Analysis of Timed Systems (FORMATS) 2019

[5] Alberto Sangiovanni-Vincentelli, Werner Damm, Roberto Passerone, Taming Dr. Frankenstein: Contract-Based Design for Cyber-Physical Systems, European Journal of Control (2012)3:217–238

SURF 2020: Hardware Implementation of Contract-Based Design for Automated Valet Parking System

2019-12-30T18:04:46Z

Jgraeben: /* Project Description */

'''[[SURF 2020|2020 SURF]]: project description'''
* Mentor: Richard M. Murray
* Co-mentor: Josefine Graebener

==Project Description==
Due to the increasing complexity of cyber-physical systems, a modular approach to the design of these systems is necessary, while also guaranteeing correct and reliable behavior. Hybrid systems with multiple layers of abstraction need to ensure the correct implementation and composition of the components. For example a system can consist of a high level decision making process, which is then refined to lower layers of abstraction, which implement the desired behavior by composing components on each layer. For this layered design to satisfy the specification, rules for the composition of the components and refinement of the layers are necessary. This is guaranteed by contracts between the layers and the components in each layer of the system, which define the accepted behavior of the overall system [5].
A system specification can be defined in terms of contracts using temporal logic as specification language, which can be formally verified [3]. Systems can be designed according to a contract-based methodology as proposed in [1]. This defines the system under normal operations, but for safety-critical systems such as self driving cars it is necessary to guarantee a specific behavior in the presence of failures as well. To achieve this we introduce reactivity into our contract design, which defines how the system will react to failure scenarios [2][4].

The example under consideration is an automatic valet parking, which shall be verified in an experimental setup. The focus of this project is the vertical contract architecture (contracts between the layers) which define the refinement of the specification including pre-defined failure scenarios.

The study objective is to set up the parking lot infrastructure in a small scale model using multiple R/C cars and verify the control strategy, which means executing the parking and retrieving process with multiple cars in the parking lot during normal operations and including failure scenarios, such as cars stopping during the process or unexpected obstacles.

The project requires some familiarity with Python or enough programming experience to learn it in a short time. Experience in experimental robotics and ROS is an advantage.

[[File:AVP.png|thumb|500px|right|Parking lot layout.]]

==References==

[1] Albert Benveniste, Benoit Caillaud, Dejan Nickovic, Roberto Passerone, Jean-Baptiste Raclet, et al.. Contracts for System Design. [Research Report] RR-8147, INRIA. 2012, pp.65. 〈hal-00757488〉

[2] P. Nuzzo, H. Xu, N. Ozay, J. B. Finn, A. L. Sangiovanni-Vincentelli, R. M. Murray, A. Donze, S. A. Seshia. A Contract-Based Methodology for Aircraft Electric Power System Design. IEEE Access, 2014. DOI 10.1109/ACCESS.2013.2295764

[3] Alberto Sangiovanni-Vincentelli, Werner Damm, Roberto Passerone, Taming Dr. Frankenstein: Contract-Based Design for Cyber-Physical Systems, European Journal of Control (2012)3:217–238

SURF 2020: Hardware Implementation of Contract-Based Design for Automated Valet Parking System

2019-12-12T03:15:42Z

Jgraeben: Created page with "==Project Description== Due to the increasing complexity of cyber-physical systems, a modular approach to the design of these systems is necessary, while also guaranteeing cor..."

==Project Description==
Due to the increasing complexity of cyber-physical systems, a modular approach to the design of these systems is necessary, while also guaranteeing correct and reliable behavior.
Hybrid systems with multiple layers of abstraction need to ensure the correct implementation and composition of the components. This is guaranteed by contracts between the layers (vertical) and the components (horizontal) [3]. A system specification can be defined in terms of contracts using temporal logic as specification language [2]. Systems can be designed according to a contract-based methodology as proposed in [1].
The example under consideration is an automatic valet parking, which shall be verified in an experimental setup.
The focus of this project is the vertical contract architecture which define the refinement of the specification including pre-defined failure scenarios.
The study objective is to set up the parking lot infrastructure with multiple agents and verify the control strategy.

The project requires some familiarity with Python or enough programming experience to learn it in a short time. Experience in experimental robotics and ROS is an advantage.

[[File:AVP.png|500px|right]]

==References==

[1] Albert Benveniste, Benoit Caillaud, Dejan Nickovic, Roberto Passerone, Jean-Baptiste Raclet, et al.. Contracts for System Design. [Research Report] RR-8147, INRIA. 2012, pp.65. 〈hal-00757488〉

[2] P. Nuzzo, H. Xu, N. Ozay, J. B. Finn, A. L. Sangiovanni-Vincentelli, R. M. Murray, A. Donze, S. A. Seshia. A Contract-Based Methodology for Aircraft Electric Power System Design. IEEE Access, 2014. DOI 10.1109/ACCESS.2013.2295764

[3] Alberto Sangiovanni-Vincentelli, Werner Damm, Roberto Passerone, Taming Dr. Frankenstein: Contract-Based Design for Cyber-Physical Systems, European Journal of Control (2012)3:217–238