APPLR: Adaptive Planner Parameter Learning from Reinforcement

TL;DR

APPLR introduces a reinforcement learning-based method for adaptive navigation parameter tuning, enabling robots to better adapt to new environments and outperform fixed or human-tuned parameters in simulation and real-world tests.

Contribution

It presents a novel RL-based approach for automatic adaptation of navigation parameters, overcoming limitations of manual tuning and demonstration-based learning.

Findings

APPLR outperforms fixed parameters in diverse environments.

It surpasses human demonstration-based tuning schemes.

Effective in both simulated and real robot experiments.

Abstract

Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. However, learning from human demonstration limits deployment to the training environment, and limits overall performance to that of a potentially-suboptimal demonstrator. In this paper, we introduce APPLR, Adaptive Planner Parameter Learning from Reinforcement, which allows existing navigation systems to adapt to new scenarios by using a parameter selection scheme discovered via reinforcement learning (RL) in a wide variety of simulation environments. We evaluate APPLR on a robot in both simulated and physical experiments, and show that it can outperform both a fixed set of hand-tuned parameters and also a dynamic parameter tuning scheme learned from human demonstration.