8 papers
Lifting Embodied World Models for Planning and Control
Alex N. Wang, Trevor Darrell, Pavel Izmailov +2
World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are high-dimensional and difficult t…
From Generated Human Videos to Physically Plausible Robot Trajectories
James Ni, Zekai Wang, Wei Lin +5
Video generation models are rapidly improving in their ability to synthesize human actions in novel contexts, holding the potential to serve as high-level planners for contextual r…
Whole-Body Conditioned Egocentric Video Prediction
Yutong Bai, Danny Tran, Amir Bar +3
We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic po…
Dual-Process Image Generation
Grace Luo, Jonathan Granskog, Aleksander Holynski +1
Prior methods for controlling image generation are limited in their ability to be taught new tasks. In contrast, vision-language models, or VLMs, can learn tasks in-context and pro…
Vision-Language Models Create Cross-Modal Task Representations
Grace Luo, Trevor Darrell, Amir Bar
Autoregressive vision-language models (VLMs) can handle many tasks within a single model, yet the representations that enable this capability remain opaque. We find that VLMs align…
Navigation World Models
Amir Bar, Gaoyue Zhou, Danny Tran +2
Navigation is a fundamental skill of agents with visual-motor capabilities. We introduce a Navigation World Model (NWM), a controllable video generation model that predicts future…