Sadigh, Dorsa

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities

Chen, B., Xu, Z., Kirmani, S., Ichter, B., Sadigh, D., Guibas, L., & Xia, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 281

SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities

Chen, B., Xu, Z., Kirmani, S., Ichter, B., Driess, D., Florence, P., Sadigh, D., Guibas, L., & Xia, F. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 269

Toward verified artificial intelligence

Seshia, S. A., Sadigh, D., & Sastry, S. S. (2022) · Communications of the ACM

Formal Methods in VerificationSafety Systems Engineering in Autonomy

Cited by 215

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

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Khazatsky, A., Pertsch, K., Nair, S., Balakrishna, A., Dasari, S., Karamcheti, S., Nasiriany, S., Srirama, M. K., Chen, L. Y., Ellis, K., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 159

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Khazatsky, A., Pertsch, K., Nair, S., Balakrishna, A., Dasari, S., Karamcheti, S., Nasiriany, S., Srirama, M., Chen, L., Ellis, K., et al. (2024) · Robotics: Science and Systems XX

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 159

Towards Verified Artificial Intelligence

Seshia, S. A., Sadigh, D., & Sastry, S. S. (2016) · arXiv

Formal Methods in VerificationSafety Systems Engineering in Autonomy

Cited by 142

Octo: An Open-Source Generalist Robot Policy

Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., Luo, J., et al. (2024) · Robotics: Science and Systems XX

Robotics and Automated SystemsReinforcement Learning in Robotics

Cited by 129

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

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 101

RT-H: Action Hierarchies Using Language

Belkhale, S., Ding, T., Xiao, T., Sermanet, P., Vuong, Q., Tompson, J., Chebotar, Y., Dwibedi, D., & Sadigh, D. (2024) · arXiv

AI-based Problem Solving and PlanningNatural Language Processing Techniques

Cited by 71

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners

Ren, A. Z., Dixit, A., Bodrova, A., Singh, S., Tu, S., Brown, N., Xu, P., Takayama, L., Xia, F., Varley, J., et al. (2023) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 69

OpenVLA: An Open-Source Vision-Language-Action Model

Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 62

Large Language Models as General Pattern Machines

Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., Rao, K., Sadigh, D., & Zeng, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

Reward Design with Language Models

Kwon, M., Xie, S. M., Bullard, K., & Sadigh, D. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 45

RoboCLIP: One Demonstration is Enough to Learn Robot Policies

Sontakke, S. A., Zhang, J., Arnold, S. M. R., Pertsch, K., Bıyık, E., Sadigh, D., Finn, C., & Itti, L. (2023) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 23

Learning Multimodal Rewards from Rankings

Myers, V., Bıyık, E., Anari, N., & Sadigh, D. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 18

Octo: An Open-Source Generalist Robot Policy

Octo Model Team, Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 18

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

Gemini Robotics: Bringing AI into the Physical World

Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., Baruch, R., Bauza, M., Blokzijl, M., et al. (2025) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 8

Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

Karamcheti, S., Nair, S., Balakrishna, A., Liang, P., Kollar, T., & Sadigh, D. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 7

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Casper, S., Davies, X., Shi, C., Krendl Gilbert, T., Scheurer, J., Rando Ramirez, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · ETH Zurich

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 3