Karl Pertsch
Karl Pertsch
UC Berkeley, Stanford University
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
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
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
Accelerating Reinforcement Learning with Learned Skill Priors
K Pertsch, Y Lee, JJ Lim
Conference on Robot Learning (CoRL), 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
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
Octo: An open-source generalist robot policy
OM Team, D Ghosh, H Walke, K Pertsch, K Black, O Mees, S Dasari, ...
arXiv preprint arXiv:2405.12213, 2024
Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
Y Chebotar, Q Vuong, K Hausman, F Xia, Y Lu, A Irpan, A Kumar, T Yu, ...
Conference on Robot Learning, 3909-3928, 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
Roboclip: One demonstration is enough to learn robot policies
S Sontakke, J Zhang, S Arnold, K Pertsch, E Bıyık, D Sadigh, C Finn, L Itti
Advances in Neural Information Processing Systems 36, 2024
Task-Induced Representation Learning
J Yamada, K Pertsch, A Gunjal, JJ Lim
International Conference on Learning Representations (ICLR), 2022, 2022
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
Transformer adapters for robot learning
A Liang, I Singh, K Pertsch, J Thomason
CoRL 2022 Workshop on Pre-training Robot Learning, 2022
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
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