OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
Slotformer: Unsupervised visual dynamics simulation with object-centric models
3 Pith papers cite this work. Polarity classification is still indexing.
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UniSim learns a universal real-world simulator from orchestrated diverse datasets, enabling zero-shot deployment of policies trained purely in simulation.
ChronoSC projects video temporal dynamics into a compact chrono-image via color stacking, transmits it with lightweight DeepJSCC, reconstructs explicitly, and applies a pre-trained BLIP model for VideoQA answers, delivering 192x bandwidth savings on CLEVRER.
citing papers explorer
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OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
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Learning Interactive Real-World Simulators
UniSim learns a universal real-world simulator from orchestrated diverse datasets, enabling zero-shot deployment of policies trained purely in simulation.
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ChronoSC: Task-Oriented Semantic Communication via Temporal-to-Color Encoding
ChronoSC projects video temporal dynamics into a compact chrono-image via color stacking, transmits it with lightweight DeepJSCC, reconstructs explicitly, and applies a pre-trained BLIP model for VideoQA answers, delivering 192x bandwidth savings on CLEVRER.