Home service robot task planning using semantic knowledge and probabilistic inference

Efficiency-Driven Adaptive Task Planning for Household Robot Based on Hierarchical Item-Environment Cognition

Task planning focused on household robots represents a conventional yet complex research domain, necessitating the development of task plans that enable robots to execute unfamiliar household services. This area has garnered significant research interest due to its extensive applications in robotics, particularly concerning household robots. Nevertheless, the majority of task planning methodologies exhibit suboptimal performance regarding the success and efficiency of completing household tasks, primarily due to a lack of cognitive capacity of household items and home environments. To address these challenges, we propose an efficiency-driven adaptive task planning approach based on hierarchical item-environment cognition. Initially, we establish a multiple semantic attribute-based priori knowledge (MSAPK) framework to facilitate the attributive representation of household items. Utilizing MSAPK, we develop a long short-term memory (LSTM) based item cognition model that assigns relevant attributes and substitutes to specified household items, thereby enhancing the cognitive capabilities of household robots at the attribute level. Subsequently, we construct an environment cognition model that delineates the relationships between household items and room types, enabling household robots to locate target items more efficiently. Through hierarchical item-environment cognition, we introduce a strategy for adaptive task planning, empowering household robots to execute household tasks with both flexibility and efficiency. The generated plans are evaluated in both virtual and real-world experiments, with promising results affirming the effectiveness of our proposed methodology.

Preferences and Expectations for Home Robot Tasks: Comparison According to Age and Household Type in Republic of Korea

Most studies of consumer preferences and expectations for home robots focus on either older adults or single-person households (SPHs). However, with the rise in voluntary SPHs among young adults and seniors, it is critical to compare both age and household types in robot research. This study explored perceptions of home robots and willingness to use their features based on age and household type, in the context of the expanding home robot market in Republic of Korea. An online survey of 400 individuals was conducted, targeting young SPHs and multi-person households (MPHs) in their 20s and 30s as well as older SPHs and MPHs in their 50s and 60s. The survey covered four robot task categories, with 40 items derived from previous research: household chores (20 items), personal care (seven items), leisure/companion (nine items), and health (four items). The results helped predict the main target groups for each in-home robot task by identifying items that showed differences in responses between groups and interpreting these based on age, household type, and their combination. The study provides valuable data on consumer expectations, highlighting differences in responses according to both age and household type, offering insights for the robotics industry to effectively target their products.

Task-Oriented Robot Cognitive Manipulation Planning Using Affordance Segmentation and Logic Reasoning

The purpose of task-oriented robot cognitive manipulation planning is to enable robots to select appropriate actions to manipulate appropriate parts of an object according to different tasks, so as to complete the human-like task execution. This ability is crucial for robots to understand how to manipulate and grasp objects under given tasks. This article proposes a task-oriented robot cognitive manipulation planning method using affordance segmentation and logic reasoning, which can provide robots with semantic reasoning skills about the most appropriate parts of the object to be manipulated and oriented by tasks. Object affordance can be obtained by constructing a convolutional neural network based on the attention mechanism. In view of the diversity of service tasks and objects in service environments, object/task ontologies are constructed to realize the management of objects and tasks, and the object-task affordances are established through causal probability logic. On this basis, the Dempster-Shafer theory is used to design a robot cognitive manipulation planning framework, which can reason manipulation regions' configuration for the intended task. The experimental results demonstrate that our proposed method can effectively improve the cognitive manipulation ability of robots and make robots preform various tasks more intelligently.