SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.
Masked autoencoders are scalable vision learners
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OceanMAE is an ocean-adapted masked autoencoder that adds physically meaningful auxiliary descriptors during self-supervised pre-training on Sentinel-2 data and shows improved marine segmentation performance on downstream benchmarks.
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Empowering Multi-Robot Cooperation via Sequential World Models
SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.
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OceanMAE: A Foundation Model for Ocean Remote Sensing
OceanMAE is an ocean-adapted masked autoencoder that adds physically meaningful auxiliary descriptors during self-supervised pre-training on Sentinel-2 data and shows improved marine segmentation performance on downstream benchmarks.