Adapting robot task planning to user preferences: an assistive shoe dressing example

The final publication is available at link.springer.comHealthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.Peer ReviewedPostprint (author's final draft

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

Teaching humanoid robotics by means of human teleoperation through RGB-D sensors

This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels

Evaluating a tactile and a tangible multi-tablet gamified quiz system for collaborative learning in primary education

[EN] Gamification has been identified as an interesting technique to foster collaboration in educational contexts. However, there are not many approaches that tackle this in primary school learning environments. The most popular technologies in the classroom are still traditional video consoles and desktop computers, which complicate the design of collaborative activities since they are essentially mono-user. The recent popularization of handheld devices such as tablets and smartphones has made it possible to build affordable, scalable, and improvised collaborative gamifled activities by creating a multi-tablet environment. In this paper we present Quizbot, a collaborative gamifled quiz application to practice different subjects, which can be defined by educators beforehand. Two versions of the system are implemented: a tactile for tablets laid on a table, in which all the elements are digital; and a tangible in which the tablets are scattered on the floor and the components are both digital and physical objects. Both versions of Quizbot are evaluated and compared in a study with eighty primary-schooled children in terms of user experience and quality of collaboration supported. Results indicate that both versions of Quizbot are essentially equally fun and easy to use, and can effectively support collaboration, with the tangible version outperforming the other one with respect to make the children reach consensus after a discussion, split and parallelize work, and treat each other with more respect, but also presenting a poorer time management.We would like to thank Universitat Politecnica de Valencia's Summer School for their collaboration during the development of this study, as well as Colegio Internacional Ausias March for their support in the development of educational content.This work is supported by Spanish Ministry of Economy and Competitiveness and funded by the European Development Regional Fund (EDRF-FEDER) with Project TIN2014-60077-R. It is also supported by fellowship ACIF/2014/214 within the VALi+d program from Conselleria d’Educació, Cultura i Esport (Generalitat Valenciana), and by fellowship FPU14/00136 within the FPU program from Spanish Ministry of Education, Culture, and SportGarcía Sanjuan, F.; El Jurdi, S.; Jaén Martínez, FJ.; Nácher-Soler, VE. (2018). Evaluating a tactile and a tangible multi-tablet gamified quiz system for collaborative learning in primary education. Computers & Education. 123:65-84. https://doi.org/10.1016/j.compedu.2018.04.011S658412

Personalization framework for adaptive robotic feeding assistance

The final publication is available at link.springer.comThe deployment of robots at home must involve robots with pre-defined skills and the capability of personalizing their behavior by non-expert users. A framework to tackle this personalization is presented and applied to an automatic feeding task. The personalization involves the caregiver providing several examples of feeding using Learning-by- Demostration, and a ProMP formalism to compute an overall trajectory and the variance along the path. Experiments show the validity of the approach in generating different feeding motions to adapt to user’s preferences, automatically extracting the relevant task parameters. The importance of the nature of the demonstrations is also assessed, and two training strategies are compared. © Springer International Publishing AG 2016.Peer ReviewedPostprint (author's final draft

Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems

As robotic systems are moved out of factory work cells into human-facing environments questions of choreography become central to their design, placement, and application. With a human viewer or counterpart present, a system will automatically be interpreted within context, style of movement, and form factor by human beings as animate elements of their environment. The interpretation by this human counterpart is critical to the success of the system's integration: knobs on the system need to make sense to a human counterpart; an artificial agent should have a way of notifying a human counterpart of a change in system state, possibly through motion profiles; and the motion of a human counterpart may have important contextual clues for task completion. Thus, professional choreographers, dance practitioners, and movement analysts are critical to research in robotics. They have design methods for movement that align with human audience perception, can identify simplified features of movement for human-robot interaction goals, and have detailed knowledge of the capacity of human movement. This article provides approaches employed by one research lab, specific impacts on technical and artistic projects within, and principles that may guide future such work. The background section reports on choreography, somatic perspectives, improvisation, the Laban/Bartenieff Movement System, and robotics. From this context methods including embodied exercises, writing prompts, and community building activities have been developed to facilitate interdisciplinary research. The results of this work is presented as an overview of a smattering of projects in areas like high-level motion planning, software development for rapid prototyping of movement, artistic output, and user studies that help understand how people interpret movement. Finally, guiding principles for other groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for the 21st Century)" http://www.mdpi.com/journal/arts/special_issues/Machine_Artis

Α Behavior Trees-based architecture towards operation planning in hybrid manufacturing

In modern manufacturing, the capability of process scheduling and task allocation is a major feature for the proper organization of complex production schedules. More particularly, the case of human-robot collaboration within assembly lines is considered as a quite challenging field, where an efficient process scheduling can reduce products’ delivery times, increasing in parallel its quality. The purpose of this paper is to propose an approach focusing on operation planning for Human-Robot Collaborative processes that consist of many tasks and multiple resources, such as the assembly of large-scale parts. The implementation of the Human-Robot Operation Planning (HROP) module is presented, which aim at the allocation of multiple operations between multiple and different types of resources. This development’s main pillar is a dynamic decision-making logic that combines both constraints, that exclude resources from the evaluation, as well as mathematical criteria, that provide finally a specific solution. The HROP particularity is that it is developed under the Behavior Trees (BT) architecture. For the validation of the proposed approach, a case study under a real industrial environment of the automotive industry is presented, based on the assembly of large-scale parts, such as buses, in a hybrid cell of both human operators and multi-type robots

Improved task planning through failure anticipation in human-robot collaboration

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe human state is defined in terms of capacity, knowledge and motivation. The system has been implemented in a standardised environment using the Planning Domain Definition Language (PDDL) and the modular ROSPlan framework, and we have validated the approach in multiple simulation settings. Our results show that using the human model fosters an appropriate task allocation while allowing failure anticipation, replanning in time to prevent it.Peer ReviewedPostprint (author's final draft