SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation
arxiv(2024)
摘要
Although visual navigation has been extensively studied using deep
reinforcement learning, online learning for real-world robots remains a
challenging task. Recent work directly learned from offline dataset to achieve
broader generalization in the real-world tasks, which, however, faces the
out-of-distribution (OOD) issue and potential robot localization failures in a
given map for unseen observation. This significantly drops the success rates
and even induces collision. In this paper, we present a self-correcting visual
navigation method, SCALE, that can autonomously prevent the robot from the OOD
situations without human intervention. Specifically, we develop an image-goal
conditioned offline reinforcement learning method based on implicit Q-learning
(IQL). When facing OOD observation, our novel localization recovery method
generates the potential future trajectories by learning from the navigation
affordance, and estimates the future novelty via random network distillation
(RND). A tailored cost function searches for the candidates with the least
novelty that can lead the robot to the familiar places. We collect offline data
and conduct evaluation experiments in three real-world urban scenarios.
Experiment results show that SCALE outperforms the previous state-of-the-art
methods for open-world navigation with a unique capability of localization
recovery, significantly reducing the need for human intervention. Code is
available at https://github.com/KubeEdge4Robotics/ScaleNav.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要