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When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan.
Planning, learning and coordination in multiagent decision processes
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The discrete brier and ranked probability skill scores
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Understanding natural language commands for robotic navigation and mobile manipulation
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Grounding english commands to reward functions
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Pragmatic language interpretation as probabilistic inference
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Cooperative inverse reinforcement learning
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Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Baker, C. L., Jara-Ettinger, J., Saxe, R., and Tenenbaum, J. B · 2017
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Probabilistic programs for inferring the goals of autonomous agents
Cusumano-Towner, M. F., Radul, A., Wingate, D., and Mansinghka, V. K · 2017
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Pragmatic-pedagogic value alignment
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Should robots be obedient?
Milli, S., Hadfield-Menell, D., Dragan, A., and Russell, S · 2017
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Nested reasoning about autonomous agents using probabilistic programs
Seaman, I. R., van de Meent, J.-W., and Wingate, D · 2018
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Reward learning from narrated demonstrations
Tung, H.-Y., Harley, A. W., Huang, L.-K., and Fragkiadaki, K · 2018
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Learning to parse natural language to grounded reward functions with weak supervision
Williams, E. C., Gopalan, N., Rhee, M., and Tellex, S · 2018
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Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources
Lieder, F. and Griffiths, T. L · 2020
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Bootstrapping an imagined we for cooperation
Tang, N., Stacy, S., Zhao, M., Marquez, G., and Gao, T · 2020
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GenParticleFilters.jl, 2020
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Online bayesian goal inference for boundedly rational planning agents
Zhi-Xuan, T., Mann, J., Silver, T., Tenenbaum, J., and Mansinghka, V · 2020
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Modeling the mistakes of boundedly rational agents within a bayesian theory of mind
Alanqary, A., Lin, G. Z., Le, J., Zhi-Xuan, T., Mansinghka, V. K., and Tenenbaum, J. B · 2021
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Modeling communication to coordinate perspectives in cooperation
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Cusumano-Towner, M. F., Saad, F. A., Lew, A. K., and Mansinghka, V. K · 2019
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Modeling Agents with Probabilistic Programs
Evans, O., Stuhlmüller, A., Salvatier, J., and Filan, D · 2019
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From language to goals: Inverse reinforcement learning for vision-based instruction following
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Theory of mind as inverse reinforcement learning
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On the feasibility of learning, rather than assuming, human biases for reward inference
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Learning a metacognition for object perception
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Too many cooks: Bayesian inference for coordinating multi-agent collaboration
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Maximum entropy inverse reinforcement learning
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Do as I can, not as I say: Grounding language in robotic affordances
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Dohan, D., Xu, W., Lewkowycz, A., Austin, J., Bieber, D., Lopes, R. G., Wu, Y., Michalewski, H., Saurous, R. A., Sohl-Dickstein, J., et al · 2022
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Lang2LTL: Translating natural language commands to temporal specification with large language models
Liu, J. X., Yang, Z., Schornstein, B., Liang, S., Idrees, I., Tellex, S., and Shah, A · 2022
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Exploring an Imagined “We” in human collective hunting: Joint commitment within shared intentionality
Tang, N., Gong, S., Zhao, M., Gu, C., Zhou, J., Shen, M., and Gao, T · 2022
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Reward design with language models
Kwon, M., Xie, S. M., Bullard, K., and Sadigh, D · 2023
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Lampp: Language models as probabilistic priors for perception and action
Li, B. Z., Chen, W., Sharma, P., and Andreas, J · 2023
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Language to rewards for robotic skill synthesis
Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.-H., Arenas, M. G., Chiang, H.-T. L., Erez, T., Hasenclever, L., Humplik, J., et al · 2023
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