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.

翻译：四足机器人作为一项持续演进的技术，目前借助模拟器开发出无需额外训练即可在现实世界中运行的鲁棒控制器。然而，由于无法预测所有可能的现实情境，本研究探索了在机器人部署后仍能持续学习的可能性。为此，我们设计了两类持续学习场景：在不同环境中对机器人进行顺序训练，同时评估其在所有环境中的表现。我们的方法揭示了前向与后向技能迁移的程度，以及机器人可能遗忘先前习得技能的幅度。通过解决这些因素，我们期望提升四足机器人在现实场景中的适应性与性能表现。