Hausman, Karol

Code as Policies: Language Model Programs for Embodied Control

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A. (2022) · arXiv

Robot Manipulation and LearningAI-based Problem Solving and Planning

Cited by 767

Code as Policies: Language Model Programs for Embodied Control

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningAI-based Problem Solving and Planning

Cited by 767

Gradient Surgery for Multi-Task Learning

Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., & Finn, C. (2020) · arXiv

Domain Adaptation and Few-Shot LearningReinforcement Learning in Robotics

Cited by 755

RT-1: Robotics Transformer for Real-World Control at Scale

Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., et al. (2022) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 727

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al. (2022) · arXiv

Smart Agriculture and AIAI-based Problem Solving and Planning

Cited by 624

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 591

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Yu, T., Quillen, D., He, Z., Julian, R., Narayan, A., Shively, H., Bellathur, A., Hausman, K., Finn, C., & Levine, S. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 450

π₀: A Vision-Language-Action Flow Model for General Robot Control

Black, K., Brown, N., Driess, D., Esmail, A., Equi, M., Finn, C., Fusai, N., Groom, L., Hausman, K., Ichter, B., et al. (2025) · Robotics: Science and Systems XXI

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 295

Inner Monologue: Embodied Reasoning through Planning with Language Models

Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al. (2022) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 287

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al. (2023) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 276

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Gupta, A., Kumar, V., Lynch, C., Levine, S., & Hausman, K. (2019) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 153

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

Active articulation model estimation through interactive perception

Hausman, K., Niekum, S., Osentoski, S., & Sukhatme, G. S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningRobotics and Sensor-Based Localization

Cited by 77

Open-World Object Manipulation using Pre-trained Vision-Language Models

Stone, A., Xiao, T., Lu, Y., Gopalakrishnan, K., Lee, K.-H., Vuong, Q., Wohlhart, P., Kirmani, S., Zitkovich, B., Xia, F., et al. (2023) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 33

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

Xiao, T., Jang, E., Kalashnikov, D., Levine, S., Ibarz, J., Hausman, K., & Herzog, A. (2020) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 25

Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

Li, C., Liang, J., Zeng, A., Chen, X., Hausman, K., Sadigh, D., Levine, S., Fei-Fei, L., Xia, F., & Ichter, B. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 16

$π_0$: A Vision-Language-Action Flow Model for General Robot Control

Black, K., Brown, N., Driess, D., Esmail, A., Equi, M., Finn, C., Fusai, N., Groom, L., Hausman, K., Ichter, B., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 13

PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Nasiriany, S., Xia, F., Yu, W., Xiao, T., Liang, J., Dasgupta, I., Xie, A., Driess, D., Wahid, A., Xu, Z., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 13

Scaling Robot Learning with Semantically Imagined Experience

Yu, T., Xiao, T., Stone, A., Tompson, J., Brohan, A., Wang, S., Singh, J., Tan, C., M, D., Peralta, J., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 4

$π_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Intelligence, P., Black, K., Brown, N., Darpinian, J., Dhabalia, K., Driess, D., Esmail, A., Equi, M., Finn, C., Fusai, N., et al. (2025) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 3