| Full text | |
| Author(s): |
Berto, Leticia M.
;
Rossi, Leonardo de L.
;
Rohmer, Eric
;
Costa, Paula D. P.
;
Simoes, Alexandre S.
;
Gudwin, Ricardo R.
;
Colombini, Esther L.
;
IEEE
Total Authors: 8
|
| Document type: | Journal article |
| Source: | 10TH IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING AND EPIGENETIC ROBOTICS (ICDL-EPIROB 2020); v. N/A, p. 7-pg., 2020-01-01. |
| Abstract | |
The advancement of technology has brought many benefits to robotics. Today, it is possible to have robots equipped with many sensors that collect different kinds of information on the environment all time. However, this brings a disadvantage: the increase of information that is received and needs to be processed. This computation is too expensive for robots and is very difficult when it has to be performed online and involves a learning process. Attention is a mechanism that can help us address the most critical data at every moment and is fundamental to improve learning. This paper discusses the importance of attention in the learning process by evaluating the possibility of learning over the attentional space. For this purpose, we modeled in a cognitive architecture the essential cognitive functions necessary to learn and used bottom-up attention as input to a reinforcement learning algorithm. The results show that the robot can learn on attentional and sensorial spaces. By comparing various action schemes, we find the set of actions for successful learning. (AU) | |
| FAPESP's process: | 16/18819-4 - Bootstrap conditions for interaction-based multimodal learning in cognitive robotics |
| Grantee: | Eric Rohmer |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |