Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric W orld K nowledge M odel ( WKM) to facilitate agent

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings.

In this paper, we investigate how to achieve stable and smooth skill chaining for long-horizon robotic manipulation tasks.

Machine Learning critically relies on the assumption that the training data is representative of the unseen instances a learner faces at test time.

Team-Nash equilibrium (TNE) predicts the outcomes of such coordinated interactions.

We study the proportional clustering problem of Chen et al. (ICML '19) and relate