DARPA Learning Introspective Control (LINC)
Learning Introspective Control (darpa.mil)
Contraction Theory (Nonlinear Stability analysis) for Machine learning + Control
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, vol. 52, 2021, pp. 135-169. (PDF)
H. Tsukamoto and S.-J. Chung, “Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach,” IEEE Control Systems Letters, vol. 5, no. 1, Jan. 2021, pp. 211-216. (PDF)
H. Tsukamoto and S.-J. Chung, “Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach,” IEEE Robotics and Automation Letters, vol. 6, no. 4, Oct. 2021, pp. 6164-6171. (PDF)
H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Neural Stochastic Contraction Metrics for Learning-based Control and Estimation,” IEEE Control Systems Letters, vol. 5, no. 5, November 2021, pp. 1825-1830. (PDF)
H. Tsukamoto and S.-J. Chung, “Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization,” IEEE Transactions on Automatic Control, vol. 66, no. 10, October 2021. pp. 4731-4746. (PDF)
Learning to Fly
M. O’Connell*, G. Shi*, X. Shi, K. Azizzadenesheli, A. Anandkumar, Y. Yue, and S.-J, Chung, “Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds,” Science Robotics, vol 7, No. 66, May 4, 2022. (Paper)
*These authors contributed equally to this work and are listed in alphabetical order.
(Caltech press release) (YouTube video 1) (YouTube video 2)
Neural Lander: Stable Drone Landing Control using Learned Dynamics
Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung
submitted to International Conference on Robotics and Automation (ICRA), 2019
We present a novel deep-learning-based robust nonlinear controller for stable quadrotor control during landing. Our approach blends together a nominal dynamics model coupled with a DNN that learns the high-order interactions, such as the complex interactions between the ground and multi-rotor airflow. To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets. [Video]
G. Shi, W. Hoenig, X. Shi, Y. Yue, and S.-J. Chung, “Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions,” IEEE Transactions on Robotics, to appear, 2021. DOI: 10.1109/TRO.2021.3098436 (PDF)
Nonlinear Adaptive Flight Control with 3D Flow Sensing
X. Shi, P. Spieler, E. Tang, E. S. Lupu, P. Tokumaru, and S.-J. Chung, “Adaptive Nonlinear Control of Fixed-Wing VTOL with Airflow Vector Sensing,” Proc. 2019 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020.
Swarm AI
Some relevant papers:
B. Riviere, W. Hoenig, M. Anderson, and S.-J. Chung, “Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments,” IEEE Robotics and Automation Letters, vol. 6, no. 4, Oct. 2021, pp. 6868-6875. (PDF)
B. Riviere and S.-J. Chung, “H-TD2: Hybrid Temporal Difference Learning for Online Optimal Urban Taxi Dispatch,” IEEE Transactions on Intelligent Transportation Systems, to appear, 2021. DOI: 10.1109/TITS.2021.3097297 (PDF)
Y. K. Nakka, A. Liu, G. Shi, A Anandkumar, Y. Yue, and S.-J. Chung, “Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems,” IEEE Robotics and Automation Letters, vol. 6, no. 2, April 2021, pp. 389-396. (PDF)
B. Rivière, W. Hoenig, Y. Yue, and S.-J. Chung, “GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning,” under review, 2020. (PDF)
G. Shi, W. Hoenig, Y. Yue, and S.-J. Chung, “Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions,” Proc. 2019 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020.