Ryan C. Julian
Ryan C. Julian
Google Brain
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Cited by
Cited by
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine
Conference on robot learning, 1094-1100, 2020
Dynamic, free-space user interactions for machine control
R Bedikian, J Marsden, K Mertens, D Holz, M Sills, M Perez, GA Hare, ...
US Patent 9,459,697, 2016
Performance analysis and terrain classification for a legged robot over rough terrain
FL Garcia Bermudez, RC Julian, DW Haldane, P Abbeel, RS Fearing
Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International …, 2012
Cooperative control and modeling for narrow passage traversal with an ornithopter MAV and lightweight ground station
RC Julian, CJ Rose, H Hu, RS Fearing
Proceedings of the 2013 international conference on Autonomous agents and …, 2013
Actionable models: Unsupervised offline reinforcement learning of robotic skills
Y Chebotar, K Hausman, Y Lu, T Xiao, D Kalashnikov, J Varley, A Irpan, ...
arXiv preprint arXiv:2104.07749, 2021
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
R Julian, B Swanson, GS Sukhatme, S Levine, C Finn, K Hausman
Conference on Robot Learning, 2020
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
Z He, R Julian, E Heiden, H Zhang, JJ Lim, S Schaal, G Sukhatme, ...
Do as i can, not as i say: Grounding language in robotic affordances
M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, ...
arXiv preprint arXiv:2204.01691, 2022
Scaling simulation-to-real transfer by learning composable robot skills
R Julian, E Heiden, Z He, H Zhang, S Schaal, J Lim, G Sukhatme, ...
International Symposium on Experimental Robotics, 267-279, 2018
Method for synchronizing operation of systems
RC Julian, HE Hongyuan, DS Holz
US Patent 9,348,419, 2016
Meta-world: a benchmark and evaluation for multi-task and meta-reinforcement learning (2019)
T Yu, D Quillen, Z He, R Julian, K Hausman, S Levine, C Finn
URL https://github. com/rlworkgroup/metaworld, 0
Scaling simulation-to-real transfer by learning a latent space of robot skills
RC Julian, E Heiden, Z He, H Zhang, S Schaal, JJ Lim, GS Sukhatme, ...
The International Journal of Robotics Research 39 (10-11), 1259-1278, 2020
Auto-conditioned recurrent mixture density networks for complex trajectory generation
H Zhang, E Heiden, R Julian, Z He, JJ Lim, GS Sukhatme
A Simple Approach to Continual Learning by Transferring Skill Parameters
KR Zentner, R Julian, U Puri, Y Zhang, GS Sukhatme
arXiv preprint arXiv:2110.10255, 2021
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning
KR Zentner, R Julian, U Puri, Y Zhang, G Sukhatme
arXiv preprint arXiv:2106.13237, 2021
Actionable Models: Unsupervised Offline Learning of Robotic Skills
A Irpan, B Eysenbach, C Finn, D Kalashnikov, J Varley, K Hausman, ...
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