PAPERS UNDER REVIEW
- I. Kim and H. Tsukamoto, “Closed-Loop Conformal Inference with VLM Parameter Prediction for Nonlinear Robust Adaptive Control,” IEEE Control Systems Letters (L-CSS), Sep. 2026.
- M. Jeong, H. Choi, H. Tsukamoto, and S. Han, “title omitted for double-blind review,” International Conference on Learning Representations (ICLR), Sep. 2026.
- S. Pandey, M. Jeong, H. Tsukamoto, and S. Han, “Control Barrier Guided Linear Steering of Latent Representations in Vision-Language-Action Models,” American Control Conference (ACC), Sep. 2026.
PEER-REVIEWED JOURNAL AND CONFERENCE PAPERS
- M. Cho, H. Tsukamoto, and H. T. Tran, “Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness,” IEEE Transactions on Robotics, Aug. 2026.
- S. Banerjee and H. Tsukamoto, “Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning,” IEEE Conference on Decision and Control (CDC), Dec. 2026.
- M. Vora, G. Puthumanaillam, H. Tsukamoto, and M. Ornik, “SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning,” Reinforcement Learning Conference, Mar. 2026.
- R. A. D’Silva and H. Tsukamoto, “Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems,” IEEE International Conference on Robotics and Automation (ICRA), Jun. 2026.
- K. Hirano and H. Tsukamoto, “Conformal Koopman for Embedded Nonlinear Control with Statistical Robustness: Theory and Real-World Validation,” IEEE International Conference on Robotics and Automation (ICRA), Jun. 2026.
- T.-W. Hsu and H. Tsukamoto, “Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety,” IEEE Control Systems Letters (L-CSS), Mar. 2025.
- S. Wei, M. Ornik, and H. Tsukamoto, “Conformal Contraction for Robust Nonlinear Control with Distribution-Free Uncertainty Quantification,” IEEE Conference on Decision and Control (CDC), Mar. 2025.
- H. Tsukamoto, S.-J. Chung, Y. K. Nakka, B. Donitz, D. Mages, and M. Ingham, “Neural-Rendezvous: Provably-robust robust guidance and control to encounter interstellar objects,” AIAA Journal of Guidance, Control, and Dynamics, Oct. 2024.
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A. Bhardwaj, S. Bhatta, and H. Tsukamoto, “Information-optimal multi-spacecraft positioning for interstellar object exploration,” IEEE Aerospace Conference, Mar. 2025.
- H. Tsukamoto*, J. Hajar*, S.-J. Chung, and F. Y. Hadaegh (*co-first authors), “Regret-optimal defense against stealthy adversaries: A system level approach,” IEEE Conference on Decision and Control (CDC), Dec. 2024.
- H. Tsukamoto, J. D. Ibrahim, J. Hajar, J. Ragan, A. Rahmani, S.-J. Chung, and F. Y. Hadaegh, “Robust optimal network topology switching for zero dynamics attacks,” IEEE Conference on Decision and Control (CDC), Dec. 2024.
- 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 (CDC), Dec. 2023.
- H. Tsukamoto, “Contraction theory for robust learning-based control: Toward aerospace and robotic autonomy,” Doctoral Dissertation, Caltech (Best Dissertation Award in Space Engineering), Jun. 2023.
- 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.
- H. Tsukamoto and S.-J. Chung, “Learning-based robust motion planning with guaranteed stability: A contraction theory approach,” IEEE Robotics and Automation Letters (RA-L), Oct. 2021 (peer-reviewed and accepted also in IEEE International Conference on Intelligent Robots and Systems, Sep. 2021).
- H. Tsukamoto, S.-J. Chung, and J.-J. E. Slotine, “Learning-based adaptive control using contraction theory,” IEEE Conference on Decision and Control (CDC), Dec. 2021.
- 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 (CDC), Dec. 2021(featured at IEEE CDC tutorial session).
- 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 (CDC), Dec. 2021 (featured at IEEE CDC tutorial session).
- 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 (L-CSS), Nov. 2021 (peer-reviewed and accepted also in American Control Conference, May 2021).
- 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 (L-CSS), Jan. 2021 (peer-reviewed and accepted also in IEEE Conference on Decision and Control, Dec. 2020).
- H. Tsukamoto and S.-J. Chung, “Convex optimization-based controller design for stochastic nonlinear systems using contraction analysis,” IEEE Conference on Decision and Control (CDC), Dec. 2019.