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

by   Hongmin Wu, et al.

Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.


What went wrong?: Identification of Everyday Object Manipulation Anomalies

Extending the abilities of service robots is important for expanding wha...

Efficiently Learning Recoveries from Failures Under Partial Observability

Operating under real world conditions is challenging due to the possibil...

Semantic Anomaly Detection with Large Language Models

As robots acquire increasingly sophisticated skills and see increasingly...

OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

Learning performant robot manipulation policies can be challenging due t...

Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion

Despite the rapid advancement of navigation algorithms, mobile robots of...

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

Robot safety has been a prominent research topic in recent years since r...

Image Captioning and Classification of Dangerous Situations

Current robot platforms are being employed to collaborate with humans in...

Please sign up or login with your details

Forgot password? Click here to reset