Open Positions @ ACXIS Lab
Autonomous Control, Exploration, Intelligence, and Systems (ACXIS)
I envision a world where anyone can follow their curiosity and turn their imagination into reality to the full extent and beyond, regardless of the physical, intellectual, and temporal constraints inherent to humans. The following is the list of our previous works and near-term future directions. Our priorities are marked in bold.
NONLINEAR CONTROL THEORY
Our Tutorial Website on Contraction Theory
H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine
Stability and Robustness
- W. Lohmiller and J.-J. E. Slotine, “On contraction analysis for nonlinear systems,” Automatica, 1998.
- H. K. Khalil, Nonlinear Systems, 3rd ed., Prentice-Hall, 2002.
- Section 2 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
Robust Control and State Estimation via Convex Optimization
- Sections 3 and 4 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
- H. Tsukamoto and S.-J. Chung, “Robust controller design for stochastic nonlinear systems via convex optimization,” IEEE Transactions on Automatic Control, Oct. 2021.
- H. Tsukamoto and S.-J. Chung, “Neural contraction metrics for robust estimation and control: A convex optimization approach,” IEEE Control Systems Letters, Jan. 2021 (peer-reviewed and accepted also in IEEE Conference on Decision and Control, Dec. 2020).
Safe Motion Planning
- S. Singh, H. Tsukamoto, B. Lopez, S.-J. Chung, and J.-J. E. Slotine, “Safe motion planning with tubes and contraction metrics,” IEEE Conference on Decision and Control, Dec. 2021(featured at IEEE CDC tutorial session).
Future Topics
- Robust Decision-Making in Systems with Various Sources of Uncertainties
- Formal Guarantees in Model-free Control
- More on DATA-DRIVEN METHODS
AEROSPACE & ROBOTIC AUTONOMY
G&C OVERVIEW of ISO Exploration
H. Tsukamoto, S.-J. Chung, B. Donitz, M. Ingham, D. Mages, and Y. K. Nakka
(3D Resources: NASA 3D Resources)
Interstellar Object Exploration (jointly with NASA JPL)
- H. Tsukamoto, S.-J. Chung, B. Donitz, M. Ingham, D. Mages, and Y. K. Nakka, “Neural-Rendezvous: Learning-based robust guidance and control to encounter interstellar objects,” submitted to AIAA Journal of Guidance, Control, and Dynamics, 2022.
- B. Donitz, D. Mages, H. Tsukamoto, P. Dixon, D. Landau, S.-J. Chung, E. Bufanda, M. Ingham, J. Castillo-Rogez, “Interstellar object accessibility and mission design,” IEEE Aerospace Conference, Mar. 2023.
Guarantees in Data-Driven Robotics (jointly with NASA JPL)
- H. Tsukamoto, B. Riviere, C. Choi, A. Rahmani, and S.-J. Chung, “CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems,” IEEE Conference on Decision and Control, Dec. 2023.
Future Topics
- Reliable Autonomy in Dynamically Hybrid Robotic Systems
- Exobiology Extant Life Surveyor (EELS), NASA JPL, coming soon in 2023 – 2024.
DATA-DRIVEN CONTROL THEORY
Our Tutorial Paper on Contraction Theory
H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine
Control Theoretical Guarantees in Data-Driven Systems
- Section 5 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
- H. Tsukamoto, S.-J. Chung, J.-J. E. Slotine, and C. Fan, “A theoretical overview of neural contraction metrics for learning-based control with guaranteed stability,” IEEE Conference on Decision and Control, Dec. 2021 (featured at IEEE CDC tutorial session).
Learning-Based Robust Control and State Estimation
- Section 6 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
- H. Tsukamoto, S.-J. Chung and J.-J. E. Slotine, “Neural stochastic contraction metrics for learning-based control and estimation,” IEEE Control Systems Letters, Nov. 2021 (peer-reviewed and accepted also in American Control Conference, May 2021).
- H. Tsukamoto and S.-J. Chung, “Neural contraction metrics for robust estimation and control: A convex optimization approach,” IEEE Control Systems Letters, Jan. 2021 (peer-reviewed and accepted also in IEEE Conference on Decision and Control, Dec. 2020).
Certified Safety and Robustness in Data-Driven Motion Planning
- H. Tsukamoto, B. Riviere, C. Choi, A. Rahmani, and S.-J. Chung, “CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems,” IEEE Conference on Decision and Control, Dec. 2023.
- Section 7 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
- H. Tsukamoto and S.-J. Chung, “Learning-based robust motion planning with guaranteed stability: A contraction theory approach,” IEEE Robotics and Automation Letters, Oct. 2021 (peer-reviewed and accepted also in IEEE International Conference on Intelligent Robots and Systems, Sep. 2021).
Robust Control of Uncertain Systems with Adaptive Online Learning
- Sections 8 and 9 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
- H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Learning-based adaptive control using contraction theory,” IEEE Conference on Decision and Control, Dec. 2021.
Future Topics
- Formal Guarantees in Data-Driven, Model-Free Control
- Performance-Guided Online Learning and Decision Making
AUTONOMOUS SYSTEMS
OUR AUTONOMY DEMONSTRATION VIDEOS
Autonomous Control, Exploration, Intelligence, and Systems (ACXIS)
(Image Credit: NASA JPL)
Formal Guarantees in Data-Driven Systems
- Section 9 of H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,” Annual Reviews in Control, Nov. 2021 (tutorial paper invited by Editor-in-Chief).
Mission Design for Interstellar Object Exploration
- B. Donitz, D. Mages, H. Tsukamoto, P. Dixon, D. Landau, S.-J. Chung, E. Bufanda, M. Ingham, J. Castillo-Rogez, “Interstellar object accessibility and mission design,” IEEE Aerospace Conference, Mar. 2023.
Future Topics
- Integrated Autonomous Design and Operation of Sub-Autonomous Systems
- Autonomous Fault Detection and Isolation/Identification (jointly with NASA JPL)
- Formal Guarantees in Data-Driven, Model-Free Systems
- System-Level Autonomy
- Task Objective-Driven Meta-Learning
