Mordatch, Igor

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,377

Improving Factuality and Reasoning in Language Models through Multiagent Debate

Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,202

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

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

Emergence of grounded compositional language in multi-agent populations

Mordatch, I., & Abbeel, P. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Language and cultural evolutionMulti-Agent Systems and Negotiation

Cited by 440

Emergent Tool Use From Multi-Agent Autocurricula

Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., & Mordatch, I. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 424

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 304

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

Learning with Opponent-Learning Awareness

Foerster, J., Chen, R. Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., & Mordatch, I. (2018) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsGame Theory and Applications

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

Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids

Mordatch, I., Lowrey, K., & Todorov, E. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Robotic Locomotion and ControlHuman Motion and Animation

Cited by 135

Implicit Behavioral Cloning

Florence, P., Lynch, C., Zeng, A., Ramirez, O., Wahid, A., Downs, L., Wong, A., Lee, J., Mordatch, I., & Tompson, J. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 131

Frozen Pretrained Transformers as Universal Computation Engines

Lu, K., Grover, A., Abbeel, P., & Mordatch, I. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 113

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

Rearrangement: A Challenge for Embodied AI

Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 109

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

Multi-Game Decision Transformers

Lee, K.-H., Nachum, O., Yang, M. (., Lee, L., Freeman, D., Guadarrama, S., Fischer, I., Xu, W., Jang, E., Michalewski, H., et al. (2022) · Advances in Neural Information Processing Systems 35

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 54

Improved Contrastive Divergence Training of Energy Based Models

Du, Y., Li, S., Tenenbaum, J., & Mordatch, I. (2020) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 36

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Leibo, J. Z., Duéñez-Guzmán, E., Vezhnevets, A. S., Agapiou, J. P., Sunehag, P., Koster, R., Matyas, J., Beattie, C., Mordatch, I., & Graepel, T. (2021) · arXiv

Reinforcement Learning in RoboticsMulti-Agent Systems and Negotiation

Cited by 31

Generative Temporal Difference Learning for Infinite-Horizon Prediction

Janner, M., Mordatch, I., & Levine, S. (2020) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 24

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10

Melting Pot 2.0

Agapiou, J. P., Vezhnevets, A. S., Duéñez-Guzmán, E. A., Matyas, J., Mao, Y., Sunehag, P., Köster, R., Madhushani, U., Kopparapu, K., Comanescu, R., et al. (2022) · arXiv

Multi-Agent Systems and NegotiationReinforcement Learning in Robotics

Cited by 3

Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains

Subramaniam, V., Du, Y., Tenenbaum, J. B., Torralba, A., Li, S., & Mordatch, I. (2025) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 2