Distributed Dynamic Hierarchical Task Assignment for Human-Robot Teams

This work implements a joint task architecture for human-robot collaborative task execution using a hierarchical task planner. This architecture allowed humans and robots to work together as teammates in the same environment while following several task constraints. These constraints are 1) sequential order, 2) non-sequential, and 3) alternative execution constraints. Both the robot and the human are aware of each other's current state and allocate their next task based on the task tree. On-table tasks, such as setting up a tea table or playing a color sequence matching game, validate the task architecture. The robot will have an updated task representation of its human teammate's task. Using this knowledge, it is also able to continuously detect the human teammate's intention towards each sub-task and coordinate it with the teammate. While performing a joint task, there can be situations in which tasks overlap or do not overlap. We designed a dialogue-based conversation between humans and robots to resolve conflict in the case of overlapping tasks.Evaluating the human-robot task architecture is the next concern after validating the task architecture. Trust and trustworthiness are some of the most critical metrics to explore. A study was conducted between humans and robots to create a homophily situation. Homophily means when a person feels biased towards another person because of having similarities in social ways. We conducted this study to determine whether humans can form a homophilic relationship with robots and whether there is a connection between homophily and trust. We found a correlation between homophily and trust in human-robot interactions.Furthermore, we designed a pipeline by which the robot learns a task by observing the human teammate's hand movement while conversing. The robot then constructs the tree by itself using a GA learning framework. Thus removing the need for manual specification by a programmer each time to revise or update the task tree which makes the architecture more flexible, realistic, efficient, and dynamic. Additionally, our architecture allows the robot to comprehend the context of a situation by conversing with a human teammate and observing the surroundings. The robot can find a link between the context of the situation and the surrounding objects by using the ontology approach and can perform the desired task accordingly. Therefore, we proposed a human-robot distributed joint task management architecture that addresses design, improvement, and evaluation under multiple constraints

Cognitive assisted living ambient system: a survey

The demographic change towards an aging population is creating a significant impact and introducing drastic challenges to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for a time. Ambient Assisted Living (AAL) is a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to impaired seniors. The paper provides a review of research background and technologies of AAL

Assistive technology design and development for acceptable robotics companions for ageing years

© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe

Artificial Cognition for Social Human-Robot Interaction: An Implementation

© 2017 The Authors Human–Robot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human. We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; human–robot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for human–robot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural human–robot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system

Three Pillar Information Management System for Modeling the Environment of Autonomous Systems

This contribution is about an information management and storage system for modeling the environment of autonomous systems. The three pillars of the system consist of prior knowledge, environment model and sensory information. The main pillar is the environment model, which supplies the autonomous system with relevant information about its current environment. For this purpose, an abstract representation of the real world is created, where instances with attributes and relations serve as virtual substitutes of entities (persons and objects) of the real world. The environment model is created based on sensory information about the real world. The gathered sensory information is typically uncertain in a stochastic sense and is represented in the environment model by means of Degree-of-Belief (DoB) distributions. The prior knowledge contains all relevant background knowledge (e.g., concepts organized in ontologies) for creating and maintaining the environment model. The concept of the three pillar information system has previously been published. Therefore this contribution focuses on further central properties of the system. Furthermore, the development status and possible applications as well as evaluation scenarios are discussed

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users