  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.
