  Quadruped robots have emerged as an evolving technology that currently
leverages simulators to develop a robust controller capable of functioning in
the real-world without the need for further training. However, since it is
impossible to predict all possible real-world situations, our research explores
the possibility of enabling them to continue learning even after their
deployment. To this end, we designed two continual learning scenarios,
sequentially training the robot on different environments while simultaneously
evaluating its performance across all of them. Our approach sheds light on the
extent of both forward and backward skill transfer, as well as the degree to
which the robot might forget previously acquired skills. By addressing these
factors, we hope to enhance the adaptability and performance of quadruped
robots in real-world scenarios.
