A service robot requires natural and interactive interaction with users without explicit commands. It is still one of the difficult problems to generate robust reactions for the robot in the real environment with unreliable sensor data to satisfy user’s requests. This paper presents an intention-response model based on mirror neuron and theory of mind, and analyzes the performance for a humanoid to show the usefulness. The model utilizes the modules of behavior selection networks to realize prompt response and goal-oriented characteristics of the mirror neuron, and performs reactions according to an action plan based on theory of mind. To cope with conflicting goals, behaviors of the sub-goal unit are generated using a hierarchical task network. Experiments with various scenarios reveal that appropriate reactions are generated according to external stimuli.
|Title of host publication||Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings|
|Editors||Kenji Doya, Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Derong Liu|
|Number of pages||9|
|Publication status||Published - 2016|
|Event||23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan|
Duration: 2016 Oct 16 → 2016 Oct 21
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||23rd International Conference on Neural Information Processing, ICONIP 2016|
|Period||16/10/16 → 16/10/21|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2016.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)