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James Harrison

jh2 _at_ stanford _dot_ edu

James Harrison

Alumni · now at Google DeepMind

James is a graduate student in the department of Mechanical Engineering. He received a B.Eng. in Mechanical Engineering from McGill University in 2015, and an M.S. in Mechanical Engineering from Stanford University in 2017. James’ research interests include control theory, robotics, and machine learning. In particular, his current work focuses on verifiably safe and robust methods for reinforcement learning, as well as unsupervised learning and representation learning in robotic task and motion planning.

Awards:

  • Office of Technology Licensing Stanford Graduate Fellowship
  • Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Scholarship

Publications

23 publications · full lab bibliography

2026
  1. L. Tresca, C. Schmidt, J. Harrison, F. Rodrigues, G. Zardini, D. Gammelli, and M. Pavone, “Robo-taxi Fleet Coordination at Scale via Reinforcement Learning,” IEEE Transactions on Control of Network Systems, 2026. (Submitted)
2025
  1. C. Schmidt, D. Gammelli, J. Harrison, M. Pavone, and F. Rodrigues, “Offline Hierarchical Reinforcement Learning via Inverse Optimization,” Int. Conf. on Learning Representations, 2025.
  2. X. Li, M. Alharbi, D. Gammelli, J. Harrison, F. Rodrigues, M. Schiffer, M. Pavone, E. Frazzoli, J. Zhao, and G. Zardini, “Reproducibility in the Control of Autonomous Mobility-on-Demand Systems,” IEEE Transactions on Robotics, 2025. (Submitted)
2023
  1. D. Gammelli, J. Harrison, K. Yang, M. Pavone, F. Rodrigues, and F. C. Pereira, “Graph Reinforcement Learning for Network Control via Bi-Level Optimization,” Int. Conf. on Machine Learning, 2023.
  2. T. Enders, J. Harrison, M. Pavone, and M. Schiffer, “Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems,” 2023.
2022
  1. R. Sinha, J. Harrison, S. M. Richards, and M. Pavone, “Adaptive Robust Model Predictive Control via Uncertainty Cancellation,” IEEE Transactions on Automatic Control, 2022. (In Press)
  2. R. Sinha, J. Harrison, S. M. Richards, and M. Pavone, “Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty,” American Control Conference, 2022.
  3. J. Willes, J. Harrison, A. Harakeh, C. Finn, M. Pavone, and S. Waslander, “Bayesian Embeddings for Few-Shot Open World Recognition,” IEEE Transactions on Pattern Analysis & Machine Intelligence, 2022.
  4. D. Gammelli, K. Yang, J. Harrison, F. Rodrigues, F. Pereira, and M. Pavone, “Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand,” ACM Int. Conf. on Knowledge Discovery and Data Mining, 2022.
  5. D. Gammelli, J. Harrison, K. Yang, M. Pavone, F. Rodrigues, and P. C. Francisco, “Graph Reinforcement Learning for Network Control via Bi-Level Optimization,” Learning on Graphs Conference, 2022.
  6. T. Lew, A. Sharma, J. Harrison, A. Bylard, and M. Pavone, “Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2888--2907, 2022.
2021
  1. J. Harrison, “Uncertainty and Efficiency in Adaptive Robot Learning and Control,” Ph.D. dissertation, Stanford University, Dept. of Mechanical Engineering, 2021.
  2. D. Gammelli, K. Yang, J. Harrison, F. Rodrigues, F. C. Pereira, and M. Pavone, “Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems,” Proc. IEEE Conf. on Decision and Control, 2021.
  3. R. Dyro, J. Harrison, A. Sharma, and M. Pavone, “Particle MPC for Uncertain and Learning-Based Control,” IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2021.
2020
  1. J. Harrison, A. Sharma, C. Finn, and M. Pavone, “Continuous Meta-Learning without Tasks,” Conf. on Neural Information Processing Systems, 2020.
  2. S. Banerjee, J. Harrison, P. M. Furlong, and M. Pavone, “Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics,” Int. Symp. on Artificial Intelligence, Robotics and Automation in Space, 2020.
  3. J. Willes, J. Harrison, A. Harakeh, C. Finn, M. Pavone, and S. Waslander, “Open-Set Incremental Learning via Bayesian Prototypical Embeddings,” Conf. on Neural Information Processing Systems - Workshop on Meta-Learning, 2020.
2019
  1. A. Sharma, J. Harrison, M. Tsao, and M. Pavone, “Robust and Adaptive Planning under Model Uncertainty,” Int. Conf. on Automated Planning and Scheduling, 2019.
  2. S. Chinchali, A. Sharma, J. Harrison, A. Elhafsi, D. Kang, E. Pergament, E. Cidon, S. Katti, and M. Pavone, “Network Offloading Policies for Cloud Robotics: a Learning-based Approach,” Robotics: Science and Systems, 2019.
  3. B. Ivanovic, J. Harrison, A. Sharma, M. Chen, and M. Pavone, “BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning,” Proc. IEEE Conf. on Robotics and Automation, 2019.
2018
  1. J. Harrison, A. Sharma, and M. Pavone, “Meta-Learning Priors for Efficient Online Bayesian Regression,” Workshop on Algorithmic Foundations of Robotics, 2018.
  2. B. Ichter, J. Harrison, and M. Pavone, “Learning Sampling Distributions for Robot Motion Planning,” Proc. IEEE Conf. on Robotics and Automation, 2018.
2017
  1. J. Harrison, A. Garg, B. Ivanovic, Y. Zhu, S. Savarese, F. F. Li, and M. Pavone, “ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems,” Int. Symp. on Robotics Research, 2017.