PlayWorld learns high-fidelity robot world models from unsupervised self-play, producing physically consistent video predictions that outperform models trained on human data and enabling 65% better real-world policy performance via model-based RL.
arXiv preprint arXiv:2012.09092 , year=
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Non-parametric closed-form bounds on counterfactual MDP transitions across compatible causal models, supporting robust policy optimization under interval uncertainty.
Ada-Diffuser is a causal diffusion model that jointly learns observed interaction structure and underlying latent dynamics from minimal observations for adaptive planning and policy learning.
citing papers explorer
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PlayWorld: Learning Robot World Models from Autonomous Play
PlayWorld learns high-fidelity robot world models from unsupervised self-play, producing physically consistent video predictions that outperform models trained on human data and enabling 65% better real-world policy performance via model-based RL.
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Robust Counterfactual Inference in Markov Decision Processes
Non-parametric closed-form bounds on counterfactual MDP transitions across compatible causal models, supporting robust policy optimization under interval uncertainty.
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Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
Ada-Diffuser is a causal diffusion model that jointly learns observed interaction structure and underlying latent dynamics from minimal observations for adaptive planning and policy learning.