Firehose

This paper proposes a new regularization technique for world models to improve planning performance by aligning latent samples with quantiles of a Gaussian distribution, which helps control heavy-tailed deviations. Practitioners caring about efficient planning in complex environments may find this approach useful.

This paper introduces a new dataset called ACE-Data-0, which provides a comprehensive and synchronized record of human behavior in real-world environments, capturing various aspects of embodied intelligence such as perception, action, and interaction. Practitioners in the field of embodied AI and machine learning can use this dataset to develop more sophisticated models that can learn from human demonstrations and perform tasks that involve complex manipulation, locomotion, and interaction.

This paper develops a new framework for world modeling that takes into account the mental state of agents, which is essential for predicting human decisions. Practitioners caring about human decision-making and planning might find this research useful.