Dimension-variable Mapless Navigation with Deep Reinforcement Learning

Understand

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).