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
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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.
Object-centric LeJEPA uses SAM object masks to extend LeJEPA's distributional objective to variable object sets and adds an instance-separating loss, outperforming image-level LeJEPA on DAVIS tracking, ImageNet classification, ADE20k segmentation, and NAVI re-identification across 10-100% of COCO da
DSSA decouples per-frame appearance from temporal identity in slot attention mechanisms to reduce slot swapping and improve temporal consistency in video object segmentation.
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.
WorldDP combines a high-level object-centric world model for subgoal planning with a low-level diffusion policy for execution, claiming better performance than baselines on multi-stage robotic manipulation benchmarks.
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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Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
WorldDP combines a high-level object-centric world model for subgoal planning with a low-level diffusion policy for execution, claiming better performance than baselines on multi-stage robotic manipulation benchmarks.