On the application of classical planning to real social robotic tasks

Pittsburgh, USA (19-20 June 2017)Automated Planning is now a mature area offering several techniques and search heuristics extremely useful to solve problems in realistic domains. However, its application to real and dynamic environments as Social Robotics requires much work focused, not only in the efficiency of the planners, but also in tractable task modeling and efficient execution and monitoring of the plan into the robotic control architecture. This paper identifies the main issues that must be taken into account while using classical Automated Planning for the control of a social robot and contributes some practical solutions to overcome such inherent difficulties. Some of them are the discrimination between predicates for internal control and external sensing, the concept of predicted nominal behavior with corrective actions or plans, the continuous monitoring of the plan execution and the handling of action interruptions. This manuscript highlights the dependencies between all the design and deployment activities involved: task modeling, plan generation, and action execution and monitoring. A task of Comprehensive Geriatric Assessment (CGA) is used as an illustrative example that can be easily generalized to any other interactive task

Challenges on the application of automated planning for comprehensive geriatric assessment using an autonomous social robot

November 22-23, 2018, Madrid, SpainComprehensive Geriatric Assessment is a medical procedure to evaluate the physical, social and psychological status of elder patients. One of its phases consists of performing different tests to the patient or relatives. In this paper we present the challenges to apply Automated Planning to control an autonomous robot helping the clinician to perform such tests. On the one hand the paper focuses on the modelling decisions taken, from an initial approach where each test was encoded using slightly different domains, to the final unified domain allowing any test to be represented. On the other hand, the paper deals with practical issues arisen when executing the plans. Preliminary tests performed with real users show that the proposed approach is able to seamlessly handle the patient-robot interaction in real time, recovering from unexpected events and adapting to the users' preferred input method, while being able to gather all the information needed by the clinician.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116) and the TIN2015-65686-C5 Spanish Ministerio de Economía y Competitividad project. Javier García is partially supported by the Comunidad de Madrid (Spain) funds under the project 2016-T2/TIC-1712

An Automated Planning Model for HRI: Use Cases on Social Assistive Robotics

Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition Language (PDDL) format, are still very hard to formalize for non expert engineers when the use case to model is complex. Human Robot Interaction (HRI) is one of those complex environments. This manuscript describes the rationale followed to design a planning model able to control social autonomous robots interacting with humans. It is the result of the authors’ experience in modeling use cases for Social Assistive Robotics (SAR) in two areas related to healthcare: Comprehensive Geriatric Assessment (CGA) and non-contact rehabilitation therapies for patients with physical impairments. In this work a general definition of these two use cases in a unique planning domain is proposed, which favors the management and integration with the software robotic architecture, as well as the addition of new use cases. Results show that the model is able to capture all the relevant aspects of the Human-Robot interaction in those scenarios, allowing the robot to autonomously perform the tasks by using a standard planning-execution architecture.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116), and grants TIN2017-88476-C2-2-R and RTI2018-099522-B-C43 of FEDER/Ministerio de Ciencia e Innovación-Ministerio de Universidades-Agencia Estatal de Investigación. Javier García is partially supported by the Comunidad de Madrid funds under the project 2016-T2/TIC-1712

Percepts symbols or Action symbols? Generalizing how all modules interact within a software architecture for cognitive robotics

Robots require a close coupling of perception and action. Cognitive robots go beyond this to require a further coupling with cognition. From the perspective of robotics, this coupling generally emphasizes a tightly integrated perceptuomotor system, which is then loosely connected to some limited form of cognitive system such as a planner. At the other end, from the perspective of automated planning, the emphasis is on a highly functional system that, taken to its extreme, calls perceptual and motor modules as independent functions. This paper proposes to join both perspectives through a unique representation where the responses of all modules on the software architecture (percepts or actions) are grounded using the same set of symbols. This allows to generalize the signal-to-symbol divide that separates classic perceptuomotor and automated planning systems, being the result a software architecture where all software modules interact using the same tokens.This paper has been partially supported by the Spanish Ministerio de Economía y Competitividad TIN2015-65686-C5 and FEDER funds and by the FP7 EU project ECHORD++ grant 601116 (CLARK project)

Cognitive Abilities in Agents

The aim of this chapter is to describe the cognitive abilities deployed on agents and multi-agent systems by using examples from applications carried out by the authors. Particularly, the following agent abilities are reviewed: problem solving, memory, decision making and learning capabilities. These abilities, which involve most of the research done in Artificial Intelligence during decades of dealing with isolated agents, are revised in order to incorporate the interaction of agents in a multi-agent environment. The results of incorporating such capabilities to agents are the enhancement of the generality and flexibility of the systems

CLARC: A cognitive robot for helping geriatric doctors in real scenarios

Third Iberian Robotics Conference (ROBOT 2017). 22 to 24 November 2017, Seville, SpainAbstract: Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate the frailty of elderly persons in order to create therapy plans that improve their quality of life. For robotizing these tests, we are designing and developing CLARC, a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Built around a shared internal representation of the outer world, the architecture is composed of software modules able to plan and generate a stream of actions, to execute actions emanated from the representation or to update this by including/removing items at different abstraction levels. Percepts, actions and intentions coming from all software modules are grounded within this unique representation. This allows the robot to react to unexpected events and to modify the course of action according to the dynamics of a scenario built around the interaction with the patient. The paper describes the architecture of the system as well as the preliminary user studies and evaluation to gather new user requirements.This work has been partially funded by the EU ECHORD++ project (FP7-ICT-601116) and the TIN2015-65686-C5-1-R (MINECO and FEDER funds). Javier García is partially supported by the Comunidad de Madrid (Spain) funds under the project 2016-T2/TIC-171

Búsqueda heurística en planificación basada en costes

En los últimos años se han producido avances importantes en el mundo de la Planificación Automática. La Planificación Automática es una rama de la Inteligencia Artificial que se centra principalmente en el estudio de técnicas computacionales genericas para resolver tareas de planificación. Las tareas de planificación típicamente consisten en obtener un conjunto ordenado de acciones, cuya ejecución permite alcanzar unos objetivos determinados. La dificultad más importante que presentan este tipo de tareas es que su resolución tiene un coste computacional muy elevado. Por este motivo, muchos planificadores utilizan heurísticas para guiar su proceso de planificación. Actualmente, una de las técnicas con más éxito es la Búsqueda Heurística. Gran parte de la investigación en Planificación Automática se ha centrado en las técnicas desde un punto de vista teórico, utilizando dominios simplificados. Estas técnicas se han ido dotando de mecanismos que las hacen cada vez más capaces de trabajar con caracteristicas más cercanas al mundo real. Entre otras, una de estas características es que las acciones suelen tener un coste asociado. La incorporación de los costes de las acciones en la tarea de planificación da lugar a la Planificación Basada en Costes. El interés por la planificación basada en costes es relativamente reciente. Así, el estudio de distintas heurísticas y las relaciones entre ellas, y de su comportamiento, bien de forma aislada o combinada, con distintos algoritmos de búsqueda es bastante limitado. Esta tesis estudia técnicas de búsqueda heurística aplicadas a planificación basada en costes. Cuenta con una parte más teórica, en la que se ha generado una heurística numérica, se han adaptado a planificación basada en costes otras heurísticas, y se ha establecido un marco teórico general que permite comparar y definir heurísticas numéricas que pertenecen a una misma clase. Asimismo, se han propuesto una serie de algoritmos de búsqueda heurítica y algunas variantes de los mismos. En la parte más experimental se ha valorado el comportamiento de los algoritmos combinados con distintas heurísticas. Como resultado, se ha obtenido una combinación heurísticas-algoritmo con un comportamiento muy adecuado en planificación basada en costes.--------------------------------------------------------------------------------------------------------Automated Planning has achieved significant progress in the last years. Automated Planning is the area of Artificial Intelligence that studies the computational techniques for solving planning tasks in a generic way. Planning tasks are tasks whose objective is to obtain an ordered set of actions such that, if applied, some objectives are reached. The most important difficulty for solving planning tasks is that their computational cost is very high. For this reason, most planners are endowed with heuristics to guide their search process. Currently, one of the most successful techniques for solving planning tasks is Heuristic Search. A great part of the research in Automated Planning have focussed on the techniques from a theoretical point of view, using simplified domains. Then, the techniques have been provided with mechanisms to be able of managing more real-world features. One such features is that, usually, actions in real-world have costs. When the action costs are incorporated in the planning task, the task is called Cost-Based Planning. The increment of the interest in Cost-Based Planning is relatively recent. Thus, the study of different heuristics, their relationships, and the behavior of such heuristics in combination with search algorithms is rather limited. This thesis studies different heuristic search techniques applied to Cost-Based Planning. From the theoretical point of view, it includes the development of a new heuristic, the adaptation of other heuristics from planning without costs, and a general theoretical framework for defining and comparing heuristics of the same class. In addition, several search algorithms and variants have been proposed for planning with action costs. From the empirical point of view, it has been performed several experiments in order to determine which combination of heuristics and algorithms are more adequate. As a result, we have obtained a heuristics-algorithm combination with very good performance

Enhancing a robotic rehabilitation model for hand-arm bimanual intensive therapy

Sevilla, 22-24 de noviembre de 2017NAOTherapist is a robotic framework that aims at developing socially-interactive rehabilitation sessions for pediatric patients with physical impairments. Although this therapeutic tool has been already assessed with the target patients in a long-term evaluation, the system is planned to participate in an Hand-Arm Bimanual Therapy Camp for Cerebral Palsy patients. This presents new challenges and requirements that must be considered to provide a better daily experience to the involved participants. This work describes how the robotic rehabilitation model used in the previous version of the platform has been improved for both the inclusion of new games and the individual adaptation.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116) and the TIN2015-65686-C5

From high to low level and vice-versa: a new language for the translation between abstraction levels in robot control architectures

The use of Planning, Execution and Monitoring architectures to control robotic platforms is becoming very popular. In most cases these architectures provide knowledge at two different levels of abstraction: high-level (deliberative planning), and low-level (robot sensing and reactive behaviours). Therefore, the translation between these two levels of abstraction is required to solve real use cases. Typically such translations are written in the source code by experts who know the software. Furthermore, if these translations or the robotic platform change, it is required to resort such experts again for editing the source code to incorporate all these changes and, in the worst case, to recompile the entire software architecture. It would be useful if such translations could be defined in a declarative way, so that they can be easily edited (even by non-experts) and without modifying the modules of the control architecture, which should be able to process such formal description. For this reason, we contribute with a language for the description of translations from High to Low and from Low to High abstraction levels when designing robotic planning tasks

CLARC: a robotic architecture for comprehensive geriatric assessment

Málaga (16-17 Junio 2016)Comprehensive Geriatric Assessment (CGA) is an integrated clinical procedure to evaluate frail old people status and create therapy plans to improve their quality and quantity of life. In this paper we present CLARC, a mobile robot able to receive the patient and his family, accompany them to the medical consulting room and, once there, help the physician to capture and manage their data during CGA procedures. The hardware structure of CLARC is based on a robotic platform from MetraLabs. The software architecture of the system incorporates a deeply tested framework for interactive robots. This framework, by encoding the whole CGA session using Automated Planning, is able to autonomously plan, drive, monitor and evaluate the session, while also managing robot navigation and data acquisition. CLARC incorporates a series of sensors allowing to collect data automatically, using non-invasive procedures. The healthcare professional can use the platform to automatically collect data while addressing other tasks such as personal interviewing, data evaluation or care planning. First trials will be carried out in hospitals in Seville and Barcelona in June and July 2016, respectively.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116) and the TIN2015-65686-C5-1-R Spanish Ministerio de Economía y Competitividad project