Learning from Demonstration (LfD) has been used in robotics research for the last decades to solve issues pertaining to conventional programming of robots. This framework enables a robot to learn a task simply from a human demonstration. However, it is unfeasible to teach a robot all possible scenarios, which may lead to e.g. the robot getting stuck. In order to solve this, a search is necessary. However, no current work is able to provide a search approach that is both simple and general. This thesis develops and evaluates a new framework based on LfD that combines both of these aspects. A single demonstration of a human search is made and a model of it is learned. From this model a search trajectory is sampled and optimized. Based on that trajectory, a prediction of the encountered environmental forces is made. An impedance controller with feed-forward of the predicted forces is then used to evaluate the algorithm on a Peg-in-Hole task. The final results show that the framework is able to successfully learn and reproduce a search from just one single human demonstration. Ultimately some suggestions are made for further benchmarks and development.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-72052 |
| Date | January 2018 |
| Creators | Ehlers, Dennis |
| Publisher | Luleå tekniska universitet, Rymdteknik, Aalto University, School of Electrical Engineering |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0024 seconds