arXiv:2606.20104v1 Announce Type: new Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions. At the same time, latent JEPA-style world models advocate learning compact predictive states from high-dimensional observations to facilitate the prediction of future states, but end-to-end training of these models is nontrivial because representations may collapse if our only goal is to construct a latent state that is easy to predict. We introduce a sensorimotor world model (SMWM): a latent world mo
The continuous advancements in AI research, particularly in areas like reinforcement learning and self-supervised methods, are pushing the boundaries of how agents perceive and interact with their environments, making this development a logical next step.
This research introduces a novel approach to building more robust and action-oriented AI world models, which is critical for developing autonomous agents capable of complex tasks in real-world scenarios.
The focus shifts from purely predictive latent states to representations that are explicitly shaped by their relevance for actions, potentially leading to more effective and less 'collapsible' AI representations.
- · AI research labs
- · Robotics companies
- · Generative AI platforms
- · Autonomous systems developers
- · AI models relying solely on visual fidelity
- · Developers struggling with representation collapse
AI agents will develop more effective and stable internal representations of their environments tailored for executing tasks.
This could accelerate the development of general-purpose humanoid robots and AI systems capable of learning new skills more efficiently.
Advances in sensorimotor learning might lead to AI systems that can adapt and perform in highly dynamic and unpredictable physical environments with greater proficiency than current models.
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Read at arXiv cs.LG