Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation Through Grounded Anomaly Classification and Recovery Policies

Abstract

Robots are poised to interact with humans in unstructured environments. Despite increasingly robust control algorithms, failure modes arise whenever the underlying dynamics are poorly modeled, especially in unstructured environments. We contribute a set of recovery policies to deal with anomalies produced by external disturbances. The recoveries work when various different types of anomalies are triggered any number of times at any point in the task, including during already running recoveries. Our recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system. Policies, skills, and introspection models are learned incrementally and contextually over time. Recoveries are studied via a collaborative kitting task where a wide range of anomalous conditions are experienced in the system. We also contribute an extensive analysis of the performance of the tightly integrated anomaly identification, classification, and recovery system under extreme anomalous conditions. We show how the integration of such a system achieves performances greater than the sum of its parts.

CLUE-AI: A Convolutional Three-Stream Anomaly Identification Framework for Robot Manipulation

“…In another work [32], a probabilistic method is proposed to predict failure cases for humanoid robots in hazardous environments by associating risks with related actions. Another study uses Hierarchical Dirichlet Process HMMs [33] to identify and classify anomalies that arise during collaborative kitting tasks. Yet another work presents a tensor voting-based method combined with support vector machines (SVMs) for classifying surface anomalies using 3D point cloud data [34].…”