The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
최근 DRL(심층 강화 학습) 방법은 목표 기반 실내 탐색 작업의 성능을 크게 향상시켰습니다. 그러나 환경의 풍부한 의미론적 정보는 여전히 이전 접근 방식에서 완전히 활용되지 않습니다. 또한 기존 방법은 일반적으로 대상 기반 탐색 작업에서 훈련 장면이나 개체에 과적합되는 경향이 있어 보이지 않는 환경에 일반화하기 어렵습니다. 인간은 자신이 보는 대상을 인식하고 경험을 통해 대상 대상의 가능한 위치를 추론할 수 있기 때문에 새로운 장면에 쉽게 적응할 수 있습니다. 이에 착안하여 Multi-View Context 정보와 Prior semantic Knowledge를 이용한 MVC-PK라는 DRL 기반의 target-driven navigation 모델을 제안한다. 대상 개체의 의미 레이블에만 의존하며 로봇이 기하 지도를 사용하지 않고 대상을 찾을 수 있습니다. 환경에서 의미론적 맥락 정보를 인식하기 위해 객체 감지기를 활용하여 다중 뷰 관찰에 존재하는 객체를 감지합니다. 실내 이동 로봇의 의미론적 추론 능력을 가능하게 하기 위해 사전 지식을 통합하기 위해 Graph Convolutional Network도 사용됩니다. 제안된 MVC-PK 모델은 AI2-THOR 시뮬레이션 환경에서 평가된다. 결과는 MVC-PK가 (1) 교차 장면 및 교차 대상 일반화 능력을 크게 향상시키고 (2) 성공률(SR) 및 성공 가중치(SPL)에서 각각 15.2% 및 11.0% 증가하는 최첨단 성능을 달성함을 보여줍니다.
Jianbing WU
Peking University
Weibo HUANG
Peking University
Guoliang HUA
Peking University
Wanruo ZHANG
Peking University
Risheng KANG
Department of Mechanical Engineering
Hong LIU
Peking University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Jianbing WU, Weibo HUANG, Guoliang HUA, Wanruo ZHANG, Risheng KANG, Hong LIU, "Semantic Path Planning for Indoor Navigation Tasks Using Multi-View Context and Prior Knowledge" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 756-764, May 2023, doi: 10.1587/transinf.2022DLP0033.
Abstract: Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0033/_p
부
@ARTICLE{e106-d_5_756,
author={Jianbing WU, Weibo HUANG, Guoliang HUA, Wanruo ZHANG, Risheng KANG, Hong LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Semantic Path Planning for Indoor Navigation Tasks Using Multi-View Context and Prior Knowledge},
year={2023},
volume={E106-D},
number={5},
pages={756-764},
abstract={Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.},
keywords={},
doi={10.1587/transinf.2022DLP0033},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Semantic Path Planning for Indoor Navigation Tasks Using Multi-View Context and Prior Knowledge
T2 - IEICE TRANSACTIONS on Information
SP - 756
EP - 764
AU - Jianbing WU
AU - Weibo HUANG
AU - Guoliang HUA
AU - Wanruo ZHANG
AU - Risheng KANG
AU - Hong LIU
PY - 2023
DO - 10.1587/transinf.2022DLP0033
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.
ER -