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Behavior Trees (BTs) are a formalism increasingly used to control the execution of robotic systems. The strength of BTs resides in their compact, hierarchical and transparent representation. However, when used in practical applications transparency is often hindered by the introduction of implicit run-time relations between nodes, e.g., because of data dependencies or hardware-related ordering constraints. Manually verifying the correctness of a BT with respect to these hidden relations is a tedious and error-prone task. This paper presents a modular planning-based approach for automatically testing BTs offline at design time, to identify possible executions that may violate given data and ordering constraints and to exhibit traces of these executions to help debugging. Our approach supports both basic and advanced BT node types, e.g., supporting parallel behaviors, and can be extended with other node types as needed. We evaluate our approach on BTs used in a commercially deployed robotics system and on a large set of randomly generated trees showing that our approach scales to realistic sizes of more than 3000 nodes., AIPlan4EU

Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS for robotics. We describe an extension of the Acumos system towards enabling the above features for general AI applications. Originally, Acumos was created for telecommunication purposes, mainly for creating linear pipelines of machine learning components. Our extensions include support for more generic components with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled solutions including control loops, sub-component topologies, and event-based communication,and provisions for assembling solutions which contain user interfaces and shared storage areas. We provide examples of deployable solutions and their interfaces. The framework is deployed at http://aiexp.ai4europe.eu/ and its source code is managed as an open source Eclipse project.

Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system's ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.

The demand for smart home technologies that enable ageingin place is rising. Through activity recognition, users’ activities can be monitored. However, for dementia patients, activity recognition alone cannot address the challenges associated with changes in the user’s habits along the disease’s stage transitions. Extending activity recognition to habit recognition enables the capturing of patients’ habits and change sin habits in order to detect anomalies. This paper aims to introduce relevant features for habit recognition solutions, extracted from data, in order to enrich the representation of the user’s habits. This solution is personalisable to meet the specific needs of the patients and generalizable for use in different scenarios. In this way caregivers are better informed on the expected changes of the patient’s habits, which can help to mitigate further deterioration through early treatment and intervention.

The way in which humans behave, speak and interact is deeply influenced by their culture. For example, greeting is done differently in France, in Sweden or in Japan; and the average interpersonal distance changes from one cultural group to the other. In order to successfully coexist with humans, robots should also adapt their behavior to the culture, customs and manners of the persons they interact with. In this paper, we deal with an important ingredient of cultural adaptation: how to generate robot plans that respect given cultural preferences, and how to execute them in a way that is sensitive to those preferences. We present initial results in this direction in the context of the CARESSES project, a joint EU-Japan effort to build culturally competent assistive robots., identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/16108

In many cases, complex multidisciplinary research projects may show a lack of coordinated development and integration, and a big effort is often required in the final phase of the projects in order to merge software developed by heterogeneous research groups. This is particularly true in advanced robotic projects: the objective here is to deliver a system that integrates all the hardware and software components, is capable of autonomous behaviour, and needs to be deployed in real-world scenarios toward providing an impact on future research and, ultimately, on society. On the other hand, in recent years there has been a growing interest for techniques related to software integration, but these have been mostly applied to the IT commercial domain. This paper presents the work performed in the context of the project CARESSES, a multidisciplinary research project focusing on socially assistive robotics that involves 9 partners from the EU and Japan. Given the complexity of the project, a huge importance has been placed on software integration, task planning and architecture definition since the first stages of the work: to this aim, some of the practices commonly used in the commercial domain for software integration, such as merging software from the early stage, have been applied. As a case study, the document describes the steps which have been followed in the first year of the project discussing strengths and weaknesses of this approach., Funding Agencies:Ministry of Internal Affairs and Communication of Japan, CARESSES

In this position paper we argue for moving towards generalpurpose domains to promote the usage of task planning forreal-world robot systems. Planning approaches should extractconcrete domains based on their current context in order tosolve problems. Towards this aim, we define the problem ofdomain reasoning, by which a planning domain is obtainedfrom a more general, multi-purpose domain definition, giventhe current deployment and context of the robot system. Weprovide examples motivating the need for domain reasoningin robot task planning, as well as a discussion of potentialsolutions to the domain reasoning problem., CARESSES

Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system’s ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.

This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home., ESS-H - Embedded Sensor Systems for Health Research Profile, ecare@home

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home., E-Care@Home

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

Configuration planning is a branch of task planning that takes into consideration both causal and information dependencies and goals.This type of planning is interesting, for instance, in smart home environmentswhich contain various sensors and robots to provide services to the inhabitants.Requests for information, for instance from an activity recognition system, should cause the smart home to configure itselfin such a way that all requested information will be providedwhen it is needed.This paper addresses temporal configuration planning in which information availability and goals are linked to temporalintervals which are subject to constrains.We propose and compare two approaches based on constraint-based planning. The first approach models information via conditions andeffects of planning operators and essentially reduces configuration planningto constraint-based temporal planning. The second approach solves information dependencies separatelyfrom task planning and optimizes the cost of reaching individual information goals.We compare these approaches in terms of thetime it takes to solve problems and the quality of the solutions they provide., E-care@home

The E-Care@Home Project aims at providing a comprehensive IoT-based healthcare system, including state-of-the-art communication protocols and high-level analysis of data from various types of sensors. With this poster, we present its novel technical infrastructure, consisting of low-power IPv6 networking, sensors for health monitoring, and resource-efficient software, that is used to gather data from elderly patients and their surrounding environment., ecare@home

Planning for robots in environments co-inhabited by humans entails handling exogenous events during plan execution. Such events require plans to be continuously adapted to ensure that they remain "human-aware", i.e., adherent to human preferences and needs. We use an approach whereby human-awareness is enforced through so-called interaction constraints. Interaction constraints are used to infer context and appropriate goals online. The current plan is modified at run time so as to achieve courses of action that are continuously human-aware. The approach is evaluated in a research facility environment in which we simulate multiple days of planning and execution., Human-Aware Task Planning for Mobile Robots

Consider a family whose home is equipped with several service robots. The actions planned for the robots must adhere to {\em Interaction Constraints (ICs)} relating them to human activities and preferences. These constraints must be sufficiently expressive to model both temporal and logical dependencies among robot actions and human behavior, and must accommodate incomplete information regarding human activities. In this paper we introduce an approach for automatically generating plans that are conformant wrt.~given ICs and partially specified human activities. The approach allows to separate causal reasoning about actions from reasoning about \ICs, and we illustrate the computational advantage this brings with experiments on a large-scale (semi-)realistic household domain with hundreds of human activities and several robots., This work was supported by the Swedish Research Council project "Human-aware task planning for mobile robots'"., Human-aware task planning for mobile robots

Safety applications require fast, precise and highly reliable sensors at low costs. This paperpresents signal processing methods for an active multispectral optical point sensorinstrumentation for which a first technical implementation exists. Due to the very demandingrequirements for safeguarding equipment, these processing methods are targeted to run on asmall embedded system with a guaranteed reaction time T < 2 ms and a sufficiently lowfailure rate according to applicable safety standards, e.g., ISO-13849. The proposed dataprocessing concept includes a novel technique for distance-aided fusion of multispectral datain order to compensate for displacement-related alteration of the measured signal. Thedistance measuring is based on triangulation with precise results even for low-resolutiondetectors, thus strengthening the practical applicability. Furthermore, standard components,such as support vector machines (SVMs), are used for reliable material classification. Allmethods have been evaluated for variants of the underlying sensor principle. Therefore, theresults of the evaluation are independent of any specific hardware.

The real-world applicability of automated planners depends on the expressiveness of the problem modeling language. Contemporary planners can deal with causal features of the problem, but only limited forms of temporal, resource and relational constraints. These constraints should be fully supported for dealing with real-world applications. We propose a highly-expressive, action-based planning language which includes causal, relational, temporal and resource constraints. This paper also contributes an approach for solving such rich planning problems by decomposition and constraint reasoning. The approach is general with respect to the types of constraints used in the problem definition language, in that additional solvers need only satisfy certain formal properties. The approach is evaluated on a domain which utilizes many features offered by the introduced language., This work was supported by the Swedish Research Council project "Human-aware task planning for mobile robots'"., Human-aware task planning for mobile robots

The main contribution of this paper is a planning language that can handle temporal constraints, resources and background knowledge. We provide a solver for this language based on problem decomposition that uses constraint satisfaction problems (CSPs) as a common ground. We argue that the usage of more expressive languages not only allows a more direct modeling of planning domains, but can speed up the planning process as well. We also present an experiment in support of that argument.