Welcome to Haruki Nishimura’s website! I am a Machine Learning Research Scientist at Toyota Research Institute. Prior to joining TRI, I was a Ph.D. student at Stanford University, advised by Prof. Mac Schwager in Multi-Robot Systems Lab. I am passionate about making embodied autonomous systems safer and more robust so people can truly trust and rely on them in the AI-assisted society of the near future. Specifically, I develop advanced trajectory planning and learning algorithms for autonomous robots that must interactively operate without failure under perceptual, dynamic, and model uncertainty. Those algorithms leverage tools from numerical optimization, optimal decision/control theory, probabilistic inference, and reinforcement learning. This TechXplore article can be a nice introduction to get a feel for the kind of research that I do.
I received my Ph.D. degree and M.S. degree from Stanford University in 2021 and 2017, respectively, and B.Eng. from the University of Tokyo in 2015, all in Aeronautics and Astronautics. At UTokyo, I was researching feature-based aerial SLAM algorithms for life-logging applications, under the supervision of Prof. Takehisa Yairi at the Research Center for Advanced Science and Technology.
Apart from research activities, I am passionate about stargazing, traveling, as well as science/engineering education.
December 11, 2023: Recognized as a Top Reviewer at NeurIPS 2023
It is such an honor to be recognized as a Top Reviewer at NeurIPS 2023!
June 15, 2023: Received Best Paper Award!
Our L4DC paper won the Best Paper Award!
April 11, 2023: Paper accepted (oral) at L4DC!
Our latest work entitled In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States has been accepted for an oral presentation at the 5th Annual Learning for Dynamics & Control Conference (L4DC). The acceptance rate for oral presentations was 9.6%. We look forward to presenting our work in Philadelphia, USA in June!
September 10, 2022: Paper accepted (oral) at CoRL!
Our latest work on risk-aware trajectory forecasting has been accepted for an oral presentation at the 2022 Conference on Robot Learning. The acceptance rate for oral presentations was 6.5%. The arXiv version is available here. We look forward to presenting our work in Auckland, New Zealand in December!
June 27, 2022: Virtual presentation at RSS 2022 workshop
I gave a short virtual presentation at RSS 2022 Workshop on Risk-Aware Decision Making, on our ongoing work of risk-aware trajectory forecasting.
September 23, 2021: Graduation
I have graduated from Stanford University! I will update my contact information after transitioning to a new career.
August 13, 2021: Paper published in IJRR
Our journal submission on fast, approximate belief space planning in continuous spaces has been published in the International Journal of Robotics Research. Check out my recent tweet for a quick summary.
July 22, 2021: Doctoral thesis defense
I have successfully defended my Ph.D. dissertation. The virtual presentation during the open session is available here.