Quantifying Teaching Behaviour in Robot Learning from Demonstration
On the map: Offline RL and imitation learning from data → Robot learning from demonstration
Abstract (arXiv)
Learning from demonstration allows for rapid deployment of robot manipulators to a great many tasks, by relying on a person showing the robot what to do rather than programming it. While this approach provides many opportunities, measuring, evaluating and improving the person's teaching ability has remained largely unexplored in robot manipulation research. To this end, a model for learning from demonstration is presented here which incorporates the teacher's understanding of, and influence on, the learner. The proposed model is used to clarify the teacher's objectives during learning from demonstration, providing new views on how teaching failures and efficiency can be defined. The benefit of this approach is shown in two experiments (N=30 and N=36, respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how ~169-180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 0 of its 48 references that are in the map. First posted on arXiv 10 May 2019.
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