164 citations · 332 across the 18 of their papers we have counts for
24 papers
Native Action-Prior Learning from Videos for World Action Models
Zhaochong An, Fei Zhang, Menglin Jia +10
World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observ…
World Action Modeling with Progressive Visual Planning
Fei Zhang, Zhaochong An, Duncan Frost +5
World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruc…
Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
Beomsu Kim, Chieh-Hsin Lai, Bac Nguyen +3
World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic memory preserves past observa…
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…
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…