Training human teacher to improve Robot Learning from Demonstration: a pilot study on Kinesthetic teaching

Maram Sakr, Martin Freeman, H. F. M. Van der Loos, Elizabeth Croft

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

10 Citations (Scopus)


Robot Learning from Demonstration (LfD) allows robots to implement autonomous manipulation by observing the movements executed by a demonstrator. As such, LfD has been established as a key element for useful user interactions in everyday environments. Kinesthetic teaching, a teaching technique within LfD, entails physically guiding the robot to achieve a task. When demonstrating complex actions on a multi-DoF manipulator, novice users typically encounter difficulties with trajectory continuity and joint orientation, necessitating training by an expert. A comparison between different training approaches is conducted in a study of nine novice users. These approaches are kinesthetic, observational and discovery-learning. The kinesthetic method utilizes record and playback functions implemented on a 7-DoF Barrett Technology WAM robot. A novice user passively holds the arm while an expert's trajectory is replayed. A visual demonstration by the expert is used for the observational training group. The discovery-learning group does not receive an expert demonstration; they use trial-and-error to produce the trajectory on their own. Task-space performance is evaluated pre- and post-training for each user to determine the relative and absolute performance improvements of the groups across the three training approaches. Absolute performance improvements are compared to the performance of an expert and a minimum-jerk trajectory to gauge how skillful the participant becomes with respect to the expert. The kinesthetic approach shows superior indicators of performance in trajectory similarity to the minimum-jerk trajectory with 39% and 13% improvement over the observational and discovery methods, respectively. Observational training shows greater improvement in terms of the smoothness of the velocity profile with 32.7% compared to 29.5% and 21.9% for both discovery and kinesthetic training, respectively.

Original languageEnglish
Title of host publication2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020)
EditorsDana Kulic, Dongheui Lee, Masahiro Shiomi
Place of PublicationNew York NY USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781728160757, 9781728160764
Publication statusPublished - 2020
EventIEEE/RSJ International Symposium on Robot and Human Interactive Communication 2020 - Virtual, Naples, Italy
Duration: 31 Aug 20204 Sept 2020
Conference number: 29th (Proceedings)


ConferenceIEEE/RSJ International Symposium on Robot and Human Interactive Communication 2020
Abbreviated titleRO-MAN 2020
Internet address

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