History of the Institut de Robòtica i Informàtica Industrial

The Institut de Robòtica i Informàtica Industrial is a Joint University Research Institute participated by the Spanish National Research Council and the Universitat Politècnica de Catalunya. Founded in 1995, its scientists have addressed over the years many research topics spanning from robot kinematics, to computer graphics, automatic control, energy systems, and human-robot interaction, among others. This book, prepared for its 25th anniversary, covers its evolution over the years, and serves as a mean of appreciation to the many students, administrative personnel, research engineers, or scientists that have formed part of it.Postprint (published version

Sharpening haptic inputs for teaching a manipulation skill to a robot

8 páginas.-- Comunicación presentada al 1st International Conference on Applied Bionics and Biomechanics celebrado en Venecia (Italia) en Octubre de 2010.Gaussian mixtures-based learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration (PbD). Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Moreover, some input variables seemed much more relevant to the particular task to be learned than others, which lead us to analyze the training data in order to select those relevant features through principal component analysis and a mutual information criterion. Then, a batch version of GMM/GMR [1], [2] was implemented using different training datasets (original, pre-processed data through PCA and MI). Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages.This work has been partially supported by the European projects PACO-PLUS (IST-4-27657) and GARNICS (FP7-247947), the Spanish project Multimodal Interaction in Pattern Recognition and Computer Vision (MIPRCV) (Consolider Ingenio 2010 project CSD2007-00018) and the Robotics group of the Generalitat de Catalunya. L. Rozo was supported by the CSIC under a JAE-PREDOC scholarship.Peer reviewe

A robot learning from demonstration framework to perform force-based manipulation tasks

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.Peer ReviewedPostprint (author’s final draft post-refereeing

Natural landmark detection for visually-guided robot navigation

The main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. Aimed at speed, our strategy has been to track salient regions along image streams by just performing on-line pixel sampling. Persistent regions are considered good candidates for landmarks, which are then characterized by a set of subregions with given color and normalized shape. They are stored in a database for posterior recognition during the navigation process. Some experimental results showing landmark-based navigation of the legged robot Lauron III in an outdoor setting are provided.Peer Reviewe

Shared task representation for human–robot collaborative navigation: the collaborative search case

© The Author(s) 2023Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human–robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human–robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human–robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human–robot team and test the acceptability and utility of different communication interface designs.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported under the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and ROCOTRANSP project (PID2019- 106702RB-C21 / AEI / 10.13039/501100011033), the European research grant TERRINet (H2020-INFRAIA-2017-1-730994) and by JST Moonshot R & D Grant Number JPMJMS2011-85.Peer ReviewedPostprint (published version

Challenge 6: Ethical, legal, economic, and social implications

In six decades of history, AI has become a mature and strategic discipline, successfully embedded in mainstream ICT and powering innumerable online applications and platforms. Several official documents stating specific AI policies have been produced by international organisations ( like the OCDE ), regional bodies ( EU ), several countries ( US, China, Spain, Germany, UK, Sweden, Brazil, Mexico...) as well as major AI-powered firms ( Google, Facebook, Amazon ). These examples demonstrate public interest and awareness of the economic and societal value of AI and the urgency of discussing the ethical, legal, economic and social implications of deploying AI systems on a massive scale. There is widespread agreement about the relevancy of addressing ethical aspects of AI, an urgency to demonstrate AI is used for the common good, and the need for better training, education and regulation to foster responsible research and innovation in AI. This chapter is organised around four main areas : ethics, law, economics and society ( ELES ). These areas shape the development of AI research and innovation, which in turn, influence these four areas of human activity. This interplay opens questions and demands new methods, objectives and ways to design future technologies. This chapter identifies the main impacts and salient challenges in each of these four areas.Peer reviewe

Better understanding motion planning: A compared review of “Principles of Robot Motion: theory, algorithms, and implementations”

The textbook on Motion Planning “Principles of Robot Motion: Theory, Algorithms, and Implementations”, by H. Choset et al., MIT Press, appeared on June 2005, is reviewed and compared to other two textbooks on the same subject, from 1991 and 2006 respectively. The ground-breaking developments over the last decade justify the necessity of the newer textbooks, that appear to be complementary, despite some overlap in the contents.Peer Reviewe

Survey on model-based manipulation planning of deformable objects

A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modeling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments. © 2011 Elsevier Ltd. All rights reserved.This work has been partially supported by the EU PACO-PLUS project FP6-2004-IST-4-27657, by the Spanish Ministry of Science and Innovation under project DPI2008-06022, and by the Catalan Research Commission through the Consolidated Robotics Group.Peer Reviewe

El aprendizaje en sistemas autónomos e inteligentes: visión general y sesgos de fuentes de datos

Autonomous and Intelligent Systems (A/IS, to adhere to the terminology of the IEEE Ethically Aligned Design report) can gather their knowledge by different means and from different sources. In principle, learning algorithms are neutral; rather, it is the data they are fed during the learning period that can introduce biases or a specific ethical orientation. Human control over the learning process is more straightforward in learning from demonstration, where data sources are restricted to the choices of the demonstrator (or teacher), but even in unsupervised versions of reinforcement learning, biases are present via the definition of the reward function. In this paper we provide an overview of learning paradigms of artificial systems: supervised and unsupervised methods, with the most striking examples in each category, without too much technical detail. Furthermore, we describe the types of data sources that are presently available and in use by the robotics community. We also focus on observable bias in image datasets and originated by human annotation. We point at quite recent research on bias in social robot navigation and end with a brief reflection about ambient influences on future learning robots.Los sistemas autónomos e inteligentes (A/IS por sus siglas en inglés, en concordancia con el informe del IEEE sobre diseño alineado con la ética) pueden obtener sus conocimientos a través de diferentes procedimientos y de fuentes diversas. Los algoritmos de aprendizaje son neutros en principio, son más bien los datos con los que se alimentan durante el período de aprendizaje que pueden introducir sesgos o una orientación ética específica. El control humano sobre el proceso de aprendizaje es más directo en aprendizaje por demostración, donde las fuentes de datos están restringidas a las elecciones del demostrador (o profesor), pero incluso en las versiones no supervisadas del aprendizaje por refuerzo los sesgos están presentes a través de la definición de la función de recompensa. En este artículo proporcionamos una visión general de los paradigmas de aprendizaje de los sistemas artificiales: métodos supervisados y no supervisados, con los ejemplos más destacados de cada categoría, sin profundizar demasiado en el detalle técnico. Además describimos los tipos de fuentes de datos disponibles actualmente y su uso por la comunidad robótica. También enfatizamos el sesgo que se observa en bases de datos de imágenes y originados por anotación humana. Destacamos una investigación muy reciente sobre sesgo en navegación de robots sociales y finalizamos con una breve reflexión sobre influencia del ambiente sobre futuros robots que aprenden

Benefits of applicability constraints in decomposition-free interference detection between nonconvex polyhedral models

IEEE International Conference on Robotics and Automation (ICRA), 1999, Detroit (EE.UU.)Non-convex polyhedral models of workpieces or robot parts can be directly tested for interference, without resorting to a previous decomposition into convex entities. Here we show that this interference detection, based on the elemental edge - face intersection test, can be performed efficiently: a computational effort reducing strategy based on applicability constraints reduces drastically the set of edge - face pairings that have to be considered for intersection. This is accomplished by using an appropriate representation, the Spherical Face Orientation Graph, developed by the authors, as well as feature pairing algorithms, based on the line sweep paradigm, that have been adapted to work on that representation. Furthermore, the benefits of such a strategy extend to the computation of a lower distance bound between the polyhedra, particularly on the quality of this lower bound. Experimental results confirm the expected advantages of this strategy.Peer Reviewe