让机器人通过个性化知识图谱更准地找家里的东西。
Enhancing Object Search in Indoor Spaces via Personalized Object-factored Ontologies
- 用个性化语义框架让机器人理解用户专属的家居认知
- 结合动态信念更新，多物品搜索准确率显著提升
- 适合长期使用的服务机器人与家庭场景研究者
个性化是服务机器人发展的关键。机器人需建立对所处环境的定制化理解，并能感知环境变化以实现长期部署。完成如准备餐桌或整理房间等复杂任务的前提是高效进行物体搜索。为此，本文提出两项核心创新：首先，设计一种新框架，使机器人能够推导出个性化的室内环境本体；该框架包含个性化机制，可调整机器人对本体的认知。其次，提出自适应推理策略，将动态信念更新融入系统，显著提升多物体搜索性能。个性化与自适应推理的协同效应增强了长期物体搜索能力。该框架基于多层语义地图实现，在真实环境中测试，并与多种前沿方法对比，验证了其有效性。此外，实验表明个性化还能作为催化剂，提升现有先进方法的表现。视频链接：https://bit.ly/3WHk9i9
原文摘要 · Abstract (English)
Personalization is critical for the advancement of service robots. Robots need to develop tailored understandings of the environments they are put in. Moreover, they need to be aware of changes in the environment to facilitate long-term deployment. Long-term understanding as well as personalization is necessary to execute complex tasks like prepare dinner table or tidy my room. A precursor to such tasks is that of Object Search. Consequently, this paper focuses on locating and searching multiple objects in indoor environments. In this paper, we propose two crucial novelties. Firstly, we propose a novel framework that can enable robots to deduce Personalized Ontologies of indoor environments. Our framework consists of a personalization schema that enables the robot to tune its understanding of ontologies. Secondly, we propose an Adaptive Inferencing strategy. We integrate Dynamic Belief Updates into our approach which improves performance in multi-object search tasks. The cumulative effect of personalization and adaptive inferencing is an improved capability in long-term object search. This framework is implemented on top of a multi-layered semantic map. We conduct experiments in real environments and compare our results against various state-of-the-art (SOTA) methods to demonstrate the effectiveness of our approach. Additionally, we show that personalization can act as a catalyst to enhance the performance of SOTAs. Video Link: https://bit.ly/3WHk9i9
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