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Learning by demonstration with critique from a human teacher (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Argall, Brenna |
| Description | In 2nd Conf. on Human-Robot Interaction (HRI Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by interacting with a teacher. In this work we present an algorithm for learning by demonstration in which the teacher operates in two phases. The teacher first demonstrates the task to the learner. The teacher next critiques learner performance of the task. This critique is used by the learner to update its control policy. In our implementation we utilize a 1-Nearest Neighbor technique which incorporates both training dataset and teacher critique. Since the teacher critiques performance only, they do not need to guess at an effective critique for the underlying algorithm. We argue that this method is particularly well-suited to human teachers, who are generally better at assigning credit to performances than to algorithms. We have applied this algorithm to the simulated task of a robot intercepting a ball. Our results demonstrate improved performance with teacher critiquing, where performance is measured by both execution success and efficiency. |
| File Format | |
| Language | English |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Simulated Task Improved Performance Natural Tool Teacher Next Critique Training Dataset 1-nearest Neighbor Technique Robot Control Policy Teacher Critique Tedious Hand-coding Effective Critique Teacher Critiquing Underlying Algorithm Human Teacher Execution Success Control Policy |
| Content Type | Text |
| Resource Type | Article |
