Karol Hausman
Karol Hausman
Google Brain, Stanford
Verified email at - Homepage
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
Gradient surgery for multi-task learning
T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn
Advances in Neural Information Processing Systems 33, 5824-5836, 2020
Learning an embedding space for transferable robot skills
K Hausman, JT Springenberg, Z Wang, N Heess, M Riedmiller
International Conference on Learning Representations, 2018
Interactive perception: Leveraging action in perception and perception in action
J Bohg, K Hausman, B Sankaran, O Brock, D Kragic, S Schaal, ...
IEEE Transactions on Robotics 33 (6), 1273-1291, 2017
Dynamics-aware unsupervised discovery of skills
A Sharma, S Gu, S Levine, V Kumar, K Hausman
arXiv preprint arXiv:1907.01657, 2019
Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor
Z Su, K Hausman, Y Chebotar, A Molchanov, GE Loeb, GS Sukhatme, ...
2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids …, 2015
Combining model-based and model-free updates for trajectory-centric reinforcement learning
Y Chebotar, K Hausman, M Zhang, G Sukhatme, S Schaal, S Levine
International conference on machine learning, 703-711, 2017
Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
A Gupta, V Kumar, C Lynch, S Levine, K Hausman
arXiv preprint arXiv:1910.11956, 2019
Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets
K Hausman, Y Chebotar, S Schaal, G Sukhatme, JJ Lim
Advances in neural information processing systems 30, 2017
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
Cooperative multi-robot control for target tracking with onboard sensing
K Hausman, J Müller, A Hariharan, N Ayanian, GS Sukhatme
The International Journal of Robotics Research 34 (13), 1660--1677, 2015
Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning
Y Chebotar, K Hausman, Z Su, GS Sukhatme, S Schaal
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016
Active Articulation Model Estimation through Interactive Perception
K Hausman, S Niekum, S Osentoski, G Sukhatme
IEEE International Conference on Robotics and Automation (ICRA) 2015, 2015
Mt-opt: Continuous multi-task robotic reinforcement learning at scale
D Kalashnikov, J Varley, Y Chebotar, B Swanson, R Jonschkowski, ...
arXiv preprint arXiv:2104.08212, 2021
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
Observability-aware trajectory optimization for self-calibration with application to uavs
K Hausman, J Preiss, GS Sukhatme, S Weiss
IEEE Robotics and Automation Letters 2 (3), 1770-1777, 2017
Tracking-based interactive segmentation of textureless objects
K Hausman, F Balint-Benczedi, D Pangercic, ZC Marton, R Ueda, ...
2013 IEEE International Conference on Robotics and Automation, 1122-1129, 2013
Self-calibrating multi-sensor fusion with probabilistic measurement validation for seamless sensor switching on a UAV
K Hausman, S Weiss, R Brockers, L Matthies, GS Sukhatme
2016 IEEE international conference on robotics and automation (ICRA), 4289-4296, 2016
Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning
R Julian, B Swanson, GS Sukhatme, S Levine, C Finn, K Hausman
arXiv preprint arXiv:2004.10190, 2020
Quantile qt-opt for risk-aware vision-based robotic grasping
C Bodnar, A Li, K Hausman, P Pastor, M Kalakrishnan
arXiv preprint arXiv:1910.02787, 2019
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