arXiv:2506.06006v3 Announce Type: replace-cross Abstract: Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.e., predicting the future state (in image form) given the previous observation and an action (in language form)? We find that VLMs struggle to generate physically plausible transitions between frames from instructions. Nevertheless, we identify a crucial asymmetry in multimodal grounding: fine-tuning a VLM to learn inverse dynamics prediction (IDP)-effectively captioning the action between frames-is significantly easier than learning FDP. In turn, IDP
This paper highlights a current limitation in generalized AI capabilities, specifically in physical world modeling, at a time when research into embodied AI and agents is rapidly progressing.
Understanding the intrinsic difficulties and potential bootstrapping methods for AI to grasp real-world physics is crucial for developing robust general AI agents and humanoid robotics.
The focus might shift from directly training forward dynamics in VLMs to leveraging inverse dynamics as an easier, intermediate step for building world models.
- · AI researchers focused on inverse dynamics
- · Developers of embodied AI systems
- · Companies investing in reinforcement learning from human feedback or physical in
- · Approaches solely reliant on direct forward dynamics prediction in VLMs
- · Companies prematurely deploying VLMs for complex physical prediction tasks
This research suggests a more effective pathway for VLMs to acquire world models by focusing on inverse dynamics first.
Improved world models could accelerate the development of more capable and reliable AI agents able to interact with complex environments.
More capable AI agents could lead to breakthroughs in areas requiring fine-grained physical manipulation and understanding, including advanced manufacturing and humanoid robotics applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI