Curriculum Vitae
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Education
- Ph.D. in Aeronautics and Astronautics, Stanford University, CA, USA, September 2021
- M.S. in Aeronautics and Astronautics, Stanford University, CA, USA, June 2017
- B.Eng. in Aeronautics and Astronautics, The University of Tokyo, Tokyo, Japan, March 2015
Scholarships & Awards
- Masason Foundation Fellowship, Tokyo, Japan
- Associate member, July 2018 – June 2019
- Member, July 2019 – February 2022
- JASSO Student Exchange Support Program, Tokyo, Japan
- Two-year graduate scholarship for Master’s students, September 2015 – June 2017
- Three-year graduate scholarship for Ph.D. students, September 2017 – August 2020
Professional Experience
- Toyota Research Institute, CA, USA, November 2021 - Present
- Research Scientist, Machine Learning
- Conducting fundamental machine learning research, with focus on planning and prediction in interactive environments.
- Toyota Research Institute, CA, USA, June 2018 – September 2018
- Intern, Object Perception
- Contributed to offline object tracking pipeline in autonomous driving software for post-processing simulation and test-driving results.
- Honda Aircraft Company, NC, USA, June 2016 – September 2016
- Intern, Systems Integration
- Improved offline flight simulation tool for non-linear aircraft analyses. Performed simulations to study characteristics of Automatic Flight Control System on HA-420 Honda Jet during approach procedure.
Research Interests
- Enabling autonomous agents to actively perceive surrounding environment for information gathering.
- Developing reliable and efficient uncertainty-aware planning algorithms for real-world robotic applications.
- Designing risk-aware and robust trajectory planning methods for safety-critical autonomous systems.
- Extending model-based control techniques to interface with modern data-driven perception and prediction modules.
Research Experience
- Multi-Robot Systems Lab (Advisor: Prof. Mac Schwager), Stanford University, September 2016 – September 2021
- Distributionally-Robust Trajectory Planning for Nonlinear Non-Gaussian Systems (RA-L 2021)
- Proposed distributionally-robust model predictive control algorithm for nonlinear dynamical systems with a Kullback-Leibler divergence constraint.
- Augmented iterative linear-exponential-quadratic-Gaussian algorithm for risk-sensitive optimal control with dynamic risk-sensitivity parameter adjustment to achieve desired level of distributional robustness.
- Fast Risk-Sensitive Planning with Deep-learned Probabilistic Prediction Models (IROS 2020)
- Proposed efficient nonlinear model predictive control algorithm to perform risk-sensitive optimal control under arbitrary generative prediction models.
- Demonstrated safe robot navigation among many mutually-interactive humans via combination of model-based stochastic control and data-driven probabilistic human trajectory forecasting.
- Fast Approximate Belief Space Planning via Stochastic Sequential Action Control (WAFR 2018, IJRR 2021)
- Developed near real-time belief space planning algorithm for control-affine nonlinear dynamical systems with stochastic jump transitions.
- Applications include information-theoretic active sensing and model-based Bayesian reinforcement learning.
- Active Vision-Based Intent Inference for Robots with Monocular Vision (ICRA 2018)
- Designed online Bayesian estimation algorithm to perform monocular vision-based intent inference for motion-based communication between two mobile robots.
- Implemented search-based belief space planning algorithm based on Monte Carlo Tree Search to plan successive camera poses to maximally reduce uncertainty in intent estimate.
- Space Application Laboratory (Advisor: Prof. Takehisa Yairi), The University of Tokyo, April 2014 – November 2014
- Aerial Simultaneous Localization and Mapping for Indoor Monitoring (Bachelor Thesis Project)
- Implemented monocular vision-based simultaneous localization and mapping algorithm with Extended Kalman Filter for Parrot AR.Drone 2.0.
- Demonstrated feasibility of life-logging and indoor monitoring using only generated feature-based map.
Publications
Chenran Li*, Chen Tang*, Haruki Nishimura, Jean Mercat, Masayoshi Tomizuka, Wei Zhan
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), December 2023
Fernando Castañeda, Haruki Nishimura, Rowan McAllister, Koushil Sreenath, Adrien Gaidon
5th Annual Learning for Dynamics & Control Conference (L4DC), June 2023
Haruki Nishimura*, Jean Mercat*, Blake Wulfe, Rowan McAllister, Adrien Gaidon
2022 Conference on Robot Learning (CoRL), December 2022
Haruki Nishimura
Ph.D. Dissertation, Stanford University, August 2021
Haruki Nishimura, Mac Schwager
The International Journal of Robotics Research (IJRR), August 2021
Haruki Nishimura, Negar Mehr, Adrien Gaidon, Mac Schwager
IEEE Robotics and Automation Letters (RA-L), January 2021
Haruki Nishimura, Boris Ivanovic, Adrien Gaidon, Marco Pavone, Mac Schwager
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2020
Haruki Nishimura, Mac Schwager
The 13th International Workshop on Algorithmic Foundations of Robotics (WAFR), December 2018
Haruki Nishimura, Mac Schwager
2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018
Talks & Presentations
June 27, 2022
Workshop presentation at Risk Aware Decision Making: From Optimal Control to Reinforcement Learning (RSS 2022 Workshop), New York City, NY, USA
July 22, 2021
Doctoral thesis defense at Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA
June 22, 2019
Invited talk at 2nd Workshop on Planning and Adaptive Sampling (RSS 2019 Workshop), Freiburg, Germany
Publication Reviewing
- Journals: IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, IEEE Transactions on Automation Science and Engineering, Journal of Aerospace Information Systems, Autonomous Robots
- Conferences: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE International Conference on Robotics and Automation (ICRA), IEEE Conference on Decision and Control (CDC), American Control Conference (ACC), Conference on Neural Information Processing Systems (NeurIPS) (Top Reviewer, 2023), Conference on Robot Learning (CoRL), International Conference on Learning Representations (ICLR)
Skills
- Software: MATLAB, Simulink, Autodesk Inventor, Autodesk 3ds Max Design
- Hardware: PIC Microcontrollers, Atmel AVR, Odroid
- Programming Languages: Julia, Python, C++, PIC Assembly Language
- Libraries and Tools: Pytorch, Docker, ROS, Gazebo, AirSim, Open AI Gym
- Soft Skills: Science and engineering lesson design based on learning sciences
- Languages: English (Professional Proficiency, TOEFL iBT 108/120), Japanese (Native)
Teaching
- Creative Engineering Project I・III, The University of Tokyo, April 2015 – July 2015
- Teaching Assistant for LETS: Learn Engineering with Tokyo University Students (Instructor: Prof. Shinji Suzuki)
- Helped teams of undergrads and grad students design and deliver science lessons to children in public elementary school in local Tokyo area.
- Gave feedback to teams based on effectiveness of lecture and experimentation design proposals as measured by knowledge transferability (concept in learning sciences).
Service and Leadership
- Stanford Japanese Association, Stanford University, CA, USA
- Officer, September 2018 – August 2020
- LETS: Learn Engineering with Tokyo University Students, The University of Tokyo, Tokyo, Japan
- Project Member, April 2013 – July 2014
- Consortium for Renovating Education of the Future, The University of Tokyo, Tokyo, Japan
- Science Workshop Planner, March 2014
- Star Watching Class in Oshika, Miyagi, Japan
- Project Leader, September 2013 – December 2013
- Non-Governmental Organization JEN, Miyagi, Japan
- Volunteer, 2011 Great East Japan Earthquake Reconstruction Assistance Program, November 2011 – October 2013
