Kinesthetic teaching and attentional supervision of structured tasks in human–robot interaction

Riccardo Caccavale, Matteo Saveriano, Alberto Finzi, Dongheui Lee

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

We present a framework that allows a robot manipulator to learn how to execute structured tasks from human demonstrations. The proposed system combines physical human–robot interaction with attentional supervision in order to support kinesthetic teaching, incremental learning, and cooperative execution of hierarchically structured tasks. In the proposed framework, the human demonstration is automatically segmented into basic movements, which are related to a task structure by an attentional system that supervises the overall interaction. The attentional system permits to track the human demonstration at different levels of abstraction and supports implicit non-verbal communication both during the teaching and the execution phase. Attention manipulation mechanisms (e.g. object and verbal cueing) can be exploited by the teacher to facilitate the learning process. On the other hand, the attentional system permits flexible and cooperative task execution. The paper describes the overall system architecture and details how cooperative tasks are learned and executed. The proposed approach is evaluated in a human–robot co-working scenario, showing that the robot is effectively able to rapidly learn and flexibly execute structured tasks.

Original languageEnglish
Pages (from-to)1291-1307
Number of pages17
JournalAutonomous Robots
Volume43
Issue number6
DOIs
StatePublished - 15 Aug 2019
Externally publishedYes

Keywords

  • Attentional supervision
  • Intuitive kinesthetic teaching
  • Learning from demonstration
  • Multimodal human–robot interaction

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