Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod F.7
Authors: Sena, Aran; Zhao, YuChen; Howard, Matthew
Title: Teaching Human Teachers to Teach Robot Learners
Abstract:
Using Programming by Demonstration to teach robot learners generalisable skills relies on having effective human teachers. This paper aims to address two problems commonly observed in demonstration data sets that arise due to poor teaching strategies; undemonstrated states and ambiguous demonstrations. Overcoming these issues through the use of visual feedback and simple heuristic rules is investigated as a potential way of guiding novice users to more effectively teach robot learners to generalise a task. The proposed method intends to offer the user a more transparent understanding of the robot learners model state during the teaching phase, to create a more interactive and robust teaching process. Results from a single-factor, three-phase repeated measures study with n = 30 participants, comparing the proposed feedback and heuristic rules set against an unguided condition, show a statistically significant (F(2, 58) = 7.952, p = 0.001) improvement of user teaching efficiency of approximately 180% when using the proposed feedback visualisation.
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod F.7
Authors: Sena, Aran; Zhao, YuChen; Howard, Matthew
Title: Teaching Human Teachers to Teach Robot Learners
Abstract:
Using Programming by Demonstration to teach robot learners generalisable skills relies on having effective human teachers. This paper aims to address two problems commonly observed in demonstration data sets that arise due to poor teaching strategies; undemonstrated states and ambiguous demonstrations. Overcoming these issues through the use of visual feedback and simple heuristic rules is investigated as a potential way of guiding novice users to more effectively teach robot learners to generalise a task. The proposed method intends to offer the user a more transparent understanding of the robot learners model state during the teaching phase, to create a more interactive and robust teaching process. Results from a single-factor, three-phase repeated measures study with n = 30 participants, comparing the proposed feedback and heuristic rules set against an unguided condition, show a statistically significant (F(2, 58) = 7.952, p = 0.001) improvement of user teaching efficiency of approximately 180% when using the proposed feedback visualisation.
