APPLD: Adaptive Planner Parameter Learning from Demonstration
This addresses the challenge for non-expert users to deploy robots in complex environments without deep technical knowledge, though it is incremental as it builds on existing navigation systems.
The paper tackles the problem of adapting autonomous robot navigation systems to new environments without requiring expert tuning, by learning parameters from human teleoperation demonstrations. The result shows that APPLD outperforms default and expert-tuned parameters, as well as the human demonstrator, in experiments on two robots with different navigation systems.
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human teleoperated demonstration of desirable navigation. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.