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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hae Won Park Howard, A.M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Hae Won Park; Howard, A.M.) |
| Abstract | A robot companion should adapt to its user's needs by learning to perform new tasks. In this paper, we present a robot playmate that learns and adapts to tasks chosen by the child on a touchscreen tablet. We aim to solve the task learning problem using an experience-based learning framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predictions of task behaviors based on prior actions. In order to automate the processes of instance encoding, acquisition, and retrieval, we have developed a framework that gathers task knowledge through interaction with human teachers. This approach, further referred to as interactive instance-based learning (IIBL), utilizes limited information available to the robot to generate similarity metrics for retrieving instances. In this paper, we focus on introducing and evaluating a new hybrid IIBL framework using sensitivity analysis with artificial neural networks and discuss its advantage over methods using k-NNs and linear regression in retrieving instances. |
| Starting Page | 6140 |
| Ending Page | 6145 |
| File Size | 3218334 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479969234 |
| DOI | 10.1109/ICRA.2015.7140061 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-26 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Artificial neural networks Training Sensitivity analysis Encoding Robot sensing systems |
| Content Type | Text |
| Resource Type | Article |
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