Our paper on self-supervised online continual learning is accepted to RA-L
페이지 정보
작성자 최고관리자 댓글 조회 작성일 24-03-26 14:17본문
Adaptive Robot Traversability Estimation Based on Self-Supervised Online Continual Learning in Unstructured Environments
Hyung-Suk Yoon* (275915), Ji-Hoon Hwang (384436), Chan Kim (266457), E-In Son (361215), Se-Wook Yoo (309483), Seung-Woo Seo (205194)
Traversability estimation is a core function for
robot navigation in off-road unstructured environments and
diverse research results have been published so far. One of the
recent approaches is using the self-supervised learning (SSL)
technique. SSL has been focused on as a breakthrough tech-
nique for situations where environments keep changing and thus
traversability estimation is a challenging task. However, most
of the research efforts based on SSL have several limitations:
(i) they operate in an offline manner that is vulnerable to the
domain distribution shift and therefore, they cannot be adaptive
to the current navigation environment; and (ii) they do not
take into consideration the aleatoric uncertainty of the dataset
which is particularly critical in unstructured environments.
In this paper, we propose an adaptive robot traversability
estimation framework that considers the current navigation
environment based on self-supervised online continual learning.
In addition, we propose an algorithm called experience replay
with uncertainty, which considers the aleatoric uncertainty
of the dataset while training the traversability estimation
model, thus enabling our framework to robustly estimate robot
traversability. We validate our methods in various real-world
environments using the Clearpath Husky robot and evaluate
that our methods show better navigation performance than
offline learning and rule-based methods. Moreover, we also
evaluate that the proposed algorithm based on experience
replay with uncertainty performs better for the benchmark
dataset (ImageNet, CORe50) than the baseline algorithms.
댓글목록
등록된 댓글이 없습니다.