  As robotics continues to advance, the need for adaptive and
continuously-learning embodied agents increases, particularly in the realm of
assistance robotics. Quick adaptability and long-term information retention are
essential to operate in dynamic environments typical of humans' everyday lives.
A lifelong learning paradigm is thus required, but it is scarcely addressed by
current robotics literature. This study empirically investigates the impact of
catastrophic forgetting and the effectiveness of knowledge transfer in neural
networks trained continuously in an embodied setting. We focus on the task of
visual odometry, which holds primary importance for embodied agents in enabling
their self-localization. We experiment on the simple continual scenario of
discrete transitions between indoor locations, akin to a robot navigating
different apartments. In this regime, we observe initial satisfactory
performance with high transferability between environments, followed by a
specialization phase where the model prioritizes current environment-specific
knowledge at the expense of generalization. Conventional regularization
strategies and increased model capacity prove ineffective in mitigating this
phenomenon. Rehearsal is instead mildly beneficial but with the addition of a
substantial memory cost. Incorporating action information, as commonly done in
embodied settings, facilitates quicker convergence but exacerbates
specialization, making the model overly reliant on its motion expectations and
less adept at correctly interpreting visual cues. These findings emphasize the
open challenges of balancing adaptation and memory retention in lifelong
robotics and contribute valuable insights into the application of a lifelong
paradigm on embodied agents.
