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This work introduces CoHuB (Collaborative Multi-Humanoid Benchmark), a simulation benchmark for multi-humanoid collaboration under egocentric visual observations and provides a foundation for developing and evaluating multi-humanoid collaboration policies.
The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can limit the plausibility...
This work argues that, given a sufficiently diverse user population, a curriculum naturally emerges between easy- and hard-to-optimize reward models, and proposes CurriPO, which grows a tree-structured curriculum to accommodate diverse user-specific objectives, covering the population in a single traversal.
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