Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
This work addresses robot robustness in unstructured environments by enabling better action identification, though it appears incremental as it builds on existing Markov switching and Bayesian nonparametric techniques.
The paper tackled the problem of robot introspection for understanding sub-task actions in unstructured environments by applying Bayesian nonparametric vector autoregressive hidden Markov models to a snap assembly task with complex dynamics, resulting in stronger generalization and better modeling of subtasks in a computationally efficient way.
Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.