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.

This work proposes a middle ground in the form of a scalable information-theoretic generalization of CCA, termed max-sliced mutual information (mSMI).

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

Evaluating machine learning models requires evaluation data.