Perceiving, Learning, and Recognizing 3D Objects: An Approach to Cognitive Service Robots


  • S. Kasaei University of Aveiro
  • Juil Sock Imperial College London
  • Luis Seabra Lopes University of Aveiro
  • Ana Maria Tome University of Aveiro
  • Tae-Kyun Kim Imperial College London



Cognitive Architectures


There is growing need for robots that can interact with people in everyday situations. For service robots, it is not reasonable to assume that one can pre-program all object categories. Instead, apart from learning from a batch of labelled training data, robots should continuously update and learn new object categories while working in the environment. This paper proposes a cognitive architecture designed to create a concurrent 3D object category learning and recognition in an interactive and open-ended manner. In particular, this cognitive architecture provides automatic perception capabilities that will allow robots to detect objects in highly crowded scenes and learn new object categories from the set of accumulated experiences in an incremental and open-ended way. Moreover, it supports constructing the full model of an unknown object in an on-line manner and predicting next best view for improving object detection and manipulation performance. We provide extensive experimental results demonstrating system performance in terms of recognition, scalability, next-best-view prediction and real-world robotic applications.




How to Cite

Kasaei, S., Sock, J., Seabra Lopes, L., Maria Tome, A., & Kim, T.-K. (2018). Perceiving, Learning, and Recognizing 3D Objects: An Approach to Cognitive Service Robots. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



AAAI Technical Track: Cognitive Modeling