Robot Task Primitive Segmentation from Demonstrations Using Only Built-in Kinematic State and Force-Torque Sensor Data

Bjært Sørensen, Simon Lyck; Savarimuthu, Thiusius Rajeeth; Iturrate, Iñigo

doi:10.1109/case56687.2023.10260448

Cited by 2 publications

(1 citation statement)

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“…The LfD algorithm is required to extract these relationships between subtasks to build the overall task logic [14,[16][17][18]22,23]. This segmentation is achieved through spatial and temporal reasoning on demonstration data [15,17,[19][20][21][22]24,[26][27][28][29][30]. Spatial features help identify subtasks, while temporal features reveal the high-level structure and sequence.…”

Section: Full Task Versus Subtask Demonstrationmentioning

confidence: 99%

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Barekatain,

Habibi,

Voos

2024

Preprint

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This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.

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