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Robust representation learning of temporal dynamic interactions is animportant problem in robotic learning in general and automated unsupervisedlearning in particular. Temporal dynamic interactions can be described by(multiple) geometric trajectories in a suitable space over which unsupervisedlearning techniques may be applied to extract useful features from raw andhigh-dimensional data measurements. Taking a geometric approach to robustrepresentation learning for temporal dynamic interactions, it is necessary todevelop suitable metrics and a systematic methodology for comparison and forassessing the stability of an unsupervised learning method with respect to itstuning parameters. Such metrics must account for the (geometric) constraints inthe physical world as well as the uncertainty associated with the learnedpatterns. In this paper we introduce a model-free metric based on theProcrustes distance for robust representation learning of interactions, and anoptimal transport based distance metric for comparing between distributions ofinteraction primitives. These distance metrics can serve as an objective forassessing the stability of an interaction learning algorithm. They are alsoused for comparing the outcomes produced by different algorithms. Moreover,they may also be adopted as an objective function to obtain clusters andrepresentative interaction primitives. These concepts and techniques will beintroduced, along with mathematical properties, while their usefulness will bedemonstrated in unsupervised learning of vehicle-to-vechicle interactionsextracted from the Safety Pilot database, the world's largest database forconnected vehicles.

Robust Unsupervised Learning of Temporal Dynamic Interactions