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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A unified taxonomy of uncertainty in ML for physics is introduced together with validation tools such as coverage, calibration, and proper scoring rules, illustrated on regression and classification tasks.
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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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Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
A unified taxonomy of uncertainty in ML for physics is introduced together with validation tools such as coverage, calibration, and proper scoring rules, illustrated on regression and classification tasks.