BNG-IRL segmentation and GMR-based anomaly detection let robots learn multi-step contact tasks from a few demonstrations, detect deviations, and incrementally add recovery behaviors.
Segmentation of Robot Movements using Position and Contact Forces
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this paper, a method for autonomous segmentation of demonstrated robot movements is proposed. Position data is clustered into Gaussian mixture models (GMMs), and an initial set of segments is identified from the Gaussian basis functions. A Kalman filter is used to detect sudden changes in the contact force/torque measurements, and this is used to update and verify the initial segmentation points. The segmentation method is verified experimentally on an industrial robot.
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Hierarchical Task Decomposition for Execution Monitoring and Error Recovery: Understanding the Rationale Behind Task Demonstrations
BNG-IRL segmentation and GMR-based anomaly detection let robots learn multi-step contact tasks from a few demonstrations, detect deviations, and incrementally add recovery behaviors.