Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals
This addresses the problem of abstract or infeasible goal specification in video-based world models for robotics, offering a more intuitive and physics-aware approach.
The paper tackles the challenge of specifying precise goals for video generation models in robotics and planning by introducing Goal Force, a framework that uses force vectors and intermediate dynamics to define goals, and demonstrates zero-shot generalization to complex real-world scenarios like tool manipulation.
Recent advancements in video generation have enabled the development of ``world models'' capable of simulating potential futures for robotics and planning. However, specifying precise goals for these models remains a challenge; text instructions are often too abstract to capture physical nuances, while target images are frequently infeasible to specify for dynamic tasks. To address this, we introduce Goal Force, a novel framework that allows users to define goals via explicit force vectors and intermediate dynamics, mirroring how humans conceptualize physical tasks. We train a video generation model on a curated dataset of synthetic causal primitives-such as elastic collisions and falling dominos-teaching it to propagate forces through time and space. Despite being trained on simple physics data, our model exhibits remarkable zero-shot generalization to complex, real-world scenarios, including tool manipulation and multi-object causal chains. Our results suggest that by grounding video generation in fundamental physical interactions, models can emerge as implicit neural physics simulators, enabling precise, physics-aware planning without reliance on external engines. We release all datasets, code, model weights, and interactive video demos at our project page.