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IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI
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We introduce Image-GOal Representations (IGOR), aiming to learn a unified, semantically consistent action space across human and various robots. Through this unified latent action space, IGOR enables knowledge transfer among large-scale robot and human activity data. We achieve this by compressing visual changes between an initial image and its goal state into latent actions. IGOR allows us to generate latent action labels for internet-scale video data. This unified latent action space enables the training of foundation policy and world models across a wide variety of tasks performed by both robots and humans. We demonstrate that: (1) IGOR learns a semantically consistent action space for both human and robots, characterizing various possible motions of objects representing the physical interaction knowledge; (2) IGOR can "migrate" the movements of the object in the one video to other videos, even across human and robots, by jointly using the latent action model and world model; (3) IGOR can learn to align latent actions with natural language through the foundation policy model, and integrate latent actions with a low-level policy model to achieve effective robot control. We believe IGOR opens new possibilities for human-to-robot knowledge transfer and control.
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Cited by 35 Pith papers
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ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
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Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training
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Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
Video Prediction Policy conditions robot action learning on future-frame predictions inside fine-tuned video diffusion models, yielding 18.6% relative gains on Calvin ABC-D and 31.6% higher real-world success rates.
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Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer
Foundation-model HOI work is organized into eight geometric, semantic, and visual sub-priors that enter six reconstruction/generation tasks and three robot-transfer routes.
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Learning Action Priors for Cross-embodiment Robot Manipulation
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LARA: Latent Action Representation Alignment for Vision-Language-Action Models
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Learning Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation
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From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
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