Active Class Selection for Few-Shot Class-Incremental Learning
Christopher McClurg Ali Ayub Harsh Tyagi Sarah M. Rajtmajer Alan R. Wagner
For real-world applications, robots will need to continually learn in their
environments through limited interactions with their users. Toward this,
previous works in few-shot class incremental learning (FSCIL) and active class
selection (ACS) have achieved promising results but were tested in constrained
setups. Therefore, in this paper, we combine ideas from FSCIL and ACS to
develop a novel framework that can allow an autonomous agent to continually
learn new objects by asking its users to label only a few of the most
informative objects in the environment. To this end, we build on a
state-of-the-art (SOTA) FSCIL model and extend it with techniques from ACS
literature. We term this model Few-shot Incremental Active class SeleCtiOn
(FIASco). We further integrate a potential field-based navigation technique
with our model to develop a complete framework that can allow an agent to
process and reason on its sensory data through the FIASco model, navigate
towards the most informative object in the environment, gather data about the
object through its sensors and incrementally update the FIASco model.
Experimental results on a simulated agent and a real robot show the
significance of our approach for long-term real-world robotics applications.