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Learning to interact with humans using goal-directed and habitual behaviors
| Content Provider | Semantic Scholar |
|---|---|
| Author | Renaudo, Erwan Devin, Sandra Girard, Benoît Chatila, Raja Alami, Rachid Khamassi, Mehdi Clodic, Aurélie |
| Copyright Year | 2015 |
| Abstract | In order to improve adaptation capabilities of robots for human-robot interaction, we take inspiration from psychology and neuroscience to propose a hybrid control architecture. This architecture is based on the multiple Experts approach that is mainly used for mammal behavior modelling. We propose to couple a human-aware task planner (HATP) with a model-free reinforcement learning to allow the robot to learn behaviors relevant to solve tasks in interaction, taking advantage from the a-priori knowledge provided to the planner and the cheap decision capability of the reinforcement learning agent. We evaluate this architecture in a HRI task of cleaning a table and show that the combination of Experts (planner and reinforcement learning agent) increases the learning speed of the learning agent. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.isir.upmc.fr/files/2015COM3503.pdf |
| Alternate Webpage(s) | https://hal.laas.fr/hal-01944380/document |
| Journal | RO-MAN 2015 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
