Kensuke Nakamura

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I am a Ph.D. student at Carnegie Mellon University’s Robotics Institute where I am advised by Prof. Andrea Bajcsy. My research leverages the synergy between optimal control and generative models to allow robots to safely operate in unstructured and uncertain environments. I develop theory and algorithms grounded in systems such as autonomous vehicles that use learned trajectory forecasters during planning or robotic manipulators that use world models to understand nuanced safety constraints. I was named a 2025 HRI Pioneer and I am fortunate to be supported by the NSF Graduate Research Fellowship.

This summer I will be interning at NVIDIA with the Autonomous Vehicle Research group led by Prof. Marco Pavone.

Previously, I graduated from Princeton University where I was advised by Jaime Fernández Fisac and Naomi Ehrich Leonard. I have also had the pleasure of collaborating with Somil Bansal.

E-mail / Google Scholar / Github / Twitter

news

Jul 27, 2026 Gave a talk at the Icon Lab at UC Berkeley and SISL lab at Stanford where I went on a deep dive discussing “when” and “how” world models can assist in robot safety.
Jul 13, 2026 Happy to have co-organized the Rethinking What It Means to be Safe For Generalist Robot Workshop at RSS 2026 this year!
Feb 11, 2026 I gave a talk to the RobIn lab at UT Austin! I talked about fundamentals of safety-critical control (Hamilton-Jacobi Reachability and Control Barrier Functions) and two works that extended these concepts to latent safety filters.
Jan 23, 2026 How to Train Your Latent Control Barrier Function got accepted as an oral presentation at L4DC 2026!
Nov 23, 2025 Excited to share that we extended the paradigm of latent safety filtering to optimization-based filters in the style of Control Barrier Functions. While theory tells us extending HJ value functions to act as CBFs should be straightforward, we identified and remedied two key details to make it work in practice. Details in the project website here

selected publications

  1. L4DC
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    How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints
    Kensuke Nakamura, Arun L. Bishop, Steven Man, and 3 more authors
    In Learning for Dynamics and Control Conference, 2026
    arXiv
  2. multisafe_overview.png
    What You Don’t Know Can Hurt You: How Well do Latent Safety Filters Understand Partially Observable Safety Constraints?
    Matthew Kim, Kensuke Nakamura, and Andrea Bajcsy
    arXiv preprint arXiv:2510.06492, 2025
    arXiv
  3. ICRA
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    AnySafe: Adapting Latent Safety Filters at Runtime via Safety Constraint Parameterization in the Latent Space
    Sankalp Agrawal, Junwon Seo, Kensuke Nakamura, and 2 more authors
    In IEEE International Conference on Robotics and Automation (ICRA), 2026
    arXiv
  4. CoRL
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    Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures
    Junwon Seo, Kensuke Nakamura, and Andrea Bajcsy
    In Conference on Robot Learning, 2025
    arXiv
  5. RSS
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    Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis
    Kensuke Nakamura, Lasse Peters, and Andrea Bajcsy
    In Robotics: Science and Systems, 2025
    arXiv
  6. CoRL
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    Not All Errors Are Made Equal: A Regret Metric for Detecting System-level Trajectory Prediction Failures
    Kensuke Nakamura, Ran Tian, and Andrea Bajcsy
    In 8th Annual Conference on Robot Learning, 2024
    arXiv
  7. CoRL
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    Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
    Haimin Hu, Zixu Zhang, Kensuke Nakamura, and 2 more authors
    In 7th Annual Conference on Robot Learning, 2023
    arXiv
  8. CDC
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    Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
    Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu, and 2 more authors
    In 2023 62nd IEEE Conference on Decision and Control (CDC), 2023
    arXiv
  9. ICRA
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    Online Update of Safety Assurances Using Confidence-Based Predictions
    Kensuke Nakamura, and Somil Bansal
    In 2023 International Conference on Robotics and Automation (ICRA), 2023
    arXiv