Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained agents are limited to the specific robot dimensions used during training, hindering their applicability when the robot's dimension changes for task-specific requirements. To overcome this limitation, we propose a dimension-variable robot navigation method based on DRL. Our approach involves training a meta agent in simulation and subsequently transferring the meta skill to a dimension-varied robot using a technique called dimension-variable skill transfer (DVST). During the training phase, the meta agent for the meta robot learns self-navigation skills with DRL. In the skill-transfer phase, observations from the dimension-varied robot are scaled and transferred to the meta agent, and the resulting control policy is scaled back to the dimension-varied robot. Through extensive simulated and real-world experiments, we demonstrated that the dimension-varied robots could successfully navigate in unknown and dynamic environments without any retraining. The results show that our work substantially expands the applicability of DRL-based navigation methods, enabling them to be used on robots with different dimensions without the limitation of a fixed dimension. The video of our experiments can be found in the supplementary file.

翻译：深度强化学习（DRL）在无地图机器人导航控制智能体训练中展现出显著潜力。然而，DRL训练的智能体受限于训练时使用的特定机器人尺寸，当机器人因任务需求改变尺寸时，其适用性受到限制。为解决这一局限，我们提出一种基于DRL的变尺寸机器人导航方法。该方法通过在仿真环境中训练元智能体，并利用称为变尺寸技能迁移（DVST）的技术，将元技能迁移至尺寸变化的机器人。在训练阶段，元机器人的元智能体通过DRL自主学习导航技能；在技能迁移阶段，尺寸变化机器人的观测数据经缩放后输入元智能体，所得控制策略再反向缩放回尺寸变化机器人。通过大量仿真与真实环境实验，我们证明尺寸变化机器人无需重新训练即可在未知动态环境中成功导航。结果表明，本方法显著拓展了基于DRL的导航方法的适用性，使其可应用于不同尺寸的机器人而无需受固定尺寸限制。实验视频详见补充文件。