James Harrison
James Harrison
Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Learning sampling distributions for robot motion planning
B Ichter, J Harrison, M Pavone
International Conference on Robotics and Automation (ICRA), 2018
Network offloading policies for cloud robotics: a learning-based approach
S Chinchali, A Sharma, J Harrison, A Elhafsi, D Kang, E Pergament, ...
Robotics: Science and Systems (RSS), 2019
Meta-learning priors for efficient online bayesian regression
J Harrison, A Sharma, M Pavone
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
A Xie, J Harrison, C Finn
International Conference on Machine Learning (ICML), 2021
Continuous meta-learning without tasks
J Harrison, A Sharma, C Finn, M Pavone
Neural Information Processing Systems (NeurIPS), 2020
BaRC: Backward reachability curriculum for robotic reinforcement learning
B Ivanovic, J Harrison, A Sharma, M Chen, M Pavone
International Conference on Robotics and Automation (ICRA), 2019
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
T Lew, A Sharma, J Harrison, A Bylard, M Pavone
Transactions on Robotics (TRO), 2022
General-purpose in-context learning by meta-learning transformers
L Kirsch, J Harrison, J Sohl-Dickstein, L Metz
arXiv preprint arXiv:2212.04458, 2022
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
D Gammelli, K Yang, J Harrison, F Rodrigues, FC Pereira, M Pavone
Conference on Decision and Control (CDC), 2021
Velo: Training versatile learned optimizers by scaling up
L Metz, J Harrison, CD Freeman, A Merchant, L Beyer, J Bradbury, ...
arXiv preprint arXiv:2211.09760, 2022
Nanoindentation studies to separate thermal and optical effects in photo-softening of azo polymers
JM Harrison, D Goldbaum, TC Corkery, CJ Barrett, RR Chromik
Journal of Materials Chemistry C 3 (5), 995-1003, 2015
Beyond human data: Scaling self-training for problem-solving with language models
A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ...
Transactions on Machine Learning Research (TMLR), 2024
Adapt: zero-shot adaptive policy transfer for stochastic dynamical systems
J Harrison, A Garg, B Ivanovic, Y Zhu, S Savarese, L Fei-Fei, M Pavone
International Symposium on Robotics Research (ISRR), 2017
Control adaptation via meta-learning dynamics
J Harrison, A Sharma, R Calandra, M Pavone
Workshop on Meta-Learning at NeurIPS 2018, 2018
Expanding the deployment envelope of behavior prediction via adaptive meta-learning
B Ivanovic, J Harrison, M Pavone
International Conference on Robotics and Automation (ICRA), 2023
Practical tradeoffs between memory, compute, and performance in learned optimizers
L Metz, CD Freeman, J Harrison, N Maheswaranathan, J Sohl-Dickstein
Conference on Lifelong Learning Agents (CoLLAs), 2022
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
R Sinha, J Harrison, SM Richards, M Pavone
American Control Conference (ACC), 2022
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
D Gammelli, K Yang, J Harrison, F Rodrigues, FC Pereira, M Pavone
Conference on Knowledge Discovery and Data Mining (KDD), 2022
Robust and adaptive planning under model uncertainty
A Sharma, J Harrison, M Tsao, M Pavone
International Conference on Automated Planning and Scheduling (ICAPS), 2019
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
T Enders, J Harrison, M Pavone, M Schiffer
Learning for Dynamics and Control (L4DC), 2023
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