Learning from demonstration of trajectory preferences through causal modeling and inference

Abstract

Learning from demonstration is associated with acquiring a solution to a task by mimicking a teacher demonstrator. Understanding the underlying reasons and in turn preferences that lead to a demonstration can yield better task comprehension. We present a generative model that describes a table-top task in terms of a causal model with respect to known concepts (e.g., the notion of a fork). Causal reasoning in the latent space of this generative model fully describes the meaning of the demonstration, e.g., that we would like to move far away from the fork. We show that by sampling from the model latent space, we can learn a solution to the problem that defines the task being demonstrated. We use a simulated kitchen tabletop environment to show changes in the underlying trajectory preference of demonstrations for different objects. The ability to generate additional data through introspection of the latent space allows us to confirm the causal model for the problem