APPLI: Adaptive Planner Parameter Learning From Interventions

TL;DR

APPLI is a method that learns to adaptively tune navigation parameters in robots based on human interventions, improving performance and generalizability over static or demonstration-based parameters.

Contribution

This work introduces APPLI, a novel approach that learns multiple navigation parameter sets from human interventions and applies them dynamically during deployment.

Findings

Improved navigation performance over static parameters

Effective adaptation in unseen environments

Successful transfer from physical to simulated tests

Abstract

While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptimal behavior in certain scenarios. The current practice in such scenarios is to manually re-tune the system's parameters, e.g. max speed, sampling rate, inflation radius, to optimize performance. This practice requires expert knowledge and may jeopardize performance in the originally good scenarios. Meanwhile, it is relatively easy for a human to identify those failure or suboptimal cases and provide a teleoperated intervention to correct the failure or suboptimal behavior. In this work, we seek to learn from those human interventions to improve navigation performance. In particular, we propose Adaptive Planner Parameter Learning from Interventions (APPLI), in which multiple sets of navigation parameters are learned during training and applied based on a confidence measure to the underlying navigation system during deployment. In our physical experiments, the robot achieves better performance compared to the planner with static default parameters, and even dynamic parameters learned from a full human demonstration. We also show APPLI's generalizability in another unseen physical test course, and a suite of 300 simulated navigation environments.