Learning from demonstration, as an important component of imitation learning, is a paradigm for robot to learn new tasks. Considering the application of learning from demonstration in the navigation issue, the robot can also acquire the navigation task via the human teacher’s demonstration. Based on research of the human brain neocortex, in this article, we present a learning from demonstration navigation paradigm from the perspective of hierarchical temporal memory theory. As a type of end-to-end learning form, the demonstrated relationship between perception data and motion commands will be learned and predicted by using hierarchical temporal memory. This framework first perceives images to obtain the corresponding categories information; then the categories incorporated with depth and motion command data are encoded as a sequence of sparse distributed representation vectors. The sequential vectors are treated as the inputs to train the navigation hierarchical temporal memory. After the training, the navigation hierarchical temporal memory stores the transitions of the perceived images, depth, and motion data so that future motion commands can be predicted. The performance of the proposed navigation strategy is evaluated via the real experiments and the public data sets.
- Cortical learning algorithm
- hierarchical temporal memory
- learning from demonstration
- sparse distributed representation
ASJC Scopus subject areas
- Computer Science Applications
- Artificial Intelligence