  Service robots are integrating more and more into our daily lives to help us
with various tasks. In such environments, robots frequently face new objects
while working in the environment and need to learn them in an open-ended
fashion. Furthermore, such robots must be able to recognize a wide range of
object categories. In this paper, we present a lifelong ensemble learning
approach based on multiple representations to address the few-shot object
recognition problem. In particular, we form ensemble methods based on deep
representations and handcrafted 3D shape descriptors. To facilitate lifelong
learning, each approach is equipped with a memory unit for storing and
retrieving object information instantly. The proposed model is suitable for
open-ended learning scenarios where the number of 3D object categories is not
fixed and can grow over time. We have performed extensive sets of experiments
to assess the performance of the proposed approach in offline, and open-ended
scenarios. For the evaluation purpose, in addition to real object datasets, we
generate a large synthetic household objects dataset consisting of 27000 views
of 90 objects. Experimental results demonstrate the effectiveness of the
proposed method on online few-shot 3D object recognition tasks, as well as its
superior performance over the state-of-the-art open-ended learning approaches.
Furthermore, our results show that while ensemble learning is modestly
beneficial in offline settings, it is significantly beneficial in lifelong
few-shot learning situations. Additionally, we demonstrated the effectiveness
of our approach in both simulated and real-robot settings, where the robot
rapidly learned new categories from limited examples.
