Karl Pertsch
Karl Pertsch
UC Berkeley, Stanford University
Verified email at - Homepage
Cited by
Cited by
Rt-1: Robotics transformer for real-world control at scale
A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ...
arXiv preprint arXiv:2212.06817, 2022
Accelerating Reinforcement Learning with Learned Skill Priors
K Pertsch, Y Lee, JJ Lim
Conference on Robot Learning (CoRL), 2020, 2020
Rt-2: Vision-language-action models transfer web knowledge to robotic control
A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ...
arXiv preprint arXiv:2307.15818, 2023
iPose: instance-aware 6D pose estimation of partly occluded objects
OH Jafari*, SK Mustikovela*, K Pertsch, E Brachmann, C Rother
Asian Conference on Computer Vision (ACCV), 2018, 2017
Demonstration-Guided Reinforcement Learning with Learned Skills
K Pertsch, Y Lee, Y Wu, JJ Lim
Conference on Robot Learning (CoRL), 2021, 2021
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
K Pertsch, O Rybkin, F Ebert, C Finn, D Jayaraman, S Levine
Conference on Neural Information Processing Systems (NeurIPS), 2020, 2020
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
J Yamada, Y Lee, G Salhotra, K Pertsch, M Pflueger, GS Sukhatme, ...
Conference on Robot Learning (CoRL), 2020, 2020
Skill-based Meta-Reinforcement Learning
T Nam, SH Sun, K Pertsch, SJ Hwang, JJ Lim
International Conference on Learning Representations (ICLR), 2022, 2022
Learning what you can do before doing anything
O Rybkin*, K Pertsch*, KG Derpanis, K Daniilidis, A Jaegle
International Conference on Learning Representations (ICLR), 2019, 2018
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
K Pertsch, O Rybkin, J Yang, K Derpanis, K Daniilidis, J Lim, A Jaegle
2nd Conference on Learning for Dynamics and Control (L4DC), 2020, 2020
Open x-embodiment: Robotic learning datasets and rt-x models
A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ...
arXiv preprint arXiv:2310.08864, 2023
Task-Induced Representation Learning
J Yamada, K Pertsch, A Gunjal, JJ Lim
International Conference on Learning Representations (ICLR), 2022, 2022
Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
Y Chebotar, Q Vuong, A Irpan, K Hausman, F Xia, Y Lu, A Kumar, T Yu, ...
arXiv preprint arXiv:2309.10150, 2023
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
S Dass, K Pertsch, H Zhang, Y Lee, JJ Lim, S Nikolaidis
arXiv preprint arXiv:2212.04708, 2022
SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
J Zhang, K Pertsch, J Zhang, JJ Lim
arXiv preprint arXiv:2306.11886, 2023
Bootstrap your own skills: Learning to solve new tasks with large language model guidance
J Zhang, J Zhang, K Pertsch, Z Liu, X Ren, M Chang, SH Sun, JJ Lim
arXiv preprint arXiv:2310.10021, 2023
Cross-Domain Transfer via Semantic Skill Imitation
K Pertsch, R Desai, V Kumar, F Meier, JJ Lim, D Batra, A Rai
Conference on Robot Learning (CoRL), 2022, 2022
Transformer adapters for robot learning
A Liang, I Singh, K Pertsch, J Thomason
CoRL 2022 Workshop on Pre-training Robot Learning, 2022
Minimum description length skills for accelerated reinforcement learning
J Zhang, K Pertsch, J Yang, JJ Lim
Self-Supervision for Reinforcement Learning Workshop-ICLR 2021, 2021
Roboclip: one demonstration is enough to learn robot policies
SA Sontakke, J Zhang, SMR Arnold, K Pertsch, E Bıyık, D Sadigh, C Finn, ...
arXiv preprint arXiv:2310.07899, 2023
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