Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments

As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by joining 3D perception with force/torque sensing, with emphasis on finding a practical solution with a feasible implementation in a domestic setting. Our algorithm obtains a point cloud representation of the working environment. From this point cloud, the garment is segmented and a custom Wrinkleness Local Descriptor (WiLD) is computed to determine the location of the present wrinkles. Using this descriptor, the most suitable ironing path is computed and, based on it, the manipulation algorithm performs the force-controlled ironing operation. Experiments have been performed with a humanoid robot platform, proving that our algorithm is able to detect successfully wrinkles present in garments and iteratively reduce the wrinkleness using an unmodified iron.Comment: Accepted and to be published on the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) that will be held in Vancouver, Canada, September 24-28, 201

A generic controller for teleoperation on robotic manipulators using low-cost devices

[Abstract] A usual form of human-robot interaction is the ability of the former to remotely command the latter through any sort of auxiliary device; this interaction is referred to with the term “teleoperation”. Robots are common examples of systems that can be controlled remotely. Depending on the task at hand, said systems can grow in complexity and costs. Specifically, the peripherals devoted to controlling the robot could require costly engineering and even an ad hoc design. However, a range of low-cost, commercial devices and controllers, originally intended for other purposes, can also be a good fit for teleoperation tasks in robotics. This work explores a selected collection of popular devices of this kind, and proposes a unified framework to exploit their capabilities as remote controllers for a set of robotic platforms. Their suitability is proven both on real and simulated versions of these platforms through simple experiments that show how they could be further used in more complex scenarios.Ministerio de Ciencia e Innovación; PID2020-113508RB-I00Comunidad de Madrid; S2018/NMT-433

Deep robot sketching: an application of deep Q-learning networks for human-like sketching

© 2023 The Authors. Published by Elsevier B.V. This research has been financed by ALMA, ‘‘Human Centric Algebraic Machine Learning’’, H2020 RIA under EU grant agreement 952091; ROBOASSET, ‘‘Sistemas robóticos inteligentes de diagnóstico y rehabilitación de terapias de miembro superior’’, PID2020-113508RBI00, financed by AEI/10.13039/501100011033; ‘‘RoboCity2030-DIHCM, Madrid Robotics Digital Innovation Hub’’, S2018/NMT-4331, financed by ‘‘Programas de Actividades I+D en la Comunidad de Madrid’’; ‘‘iREHAB: AI-powered Robotic Personalized Rehabilitation’’, ISCIIIAES-2022/003041 financed by ISCIII and UE; and EU structural fundsThe current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs.Sección Deptal. de Arquitectura de Computadores y Automática (Físicas)Fac. de Ciencias FísicasTRUEUnión Europea. H2020Ministerio de Ciencia e Innovación (MICINN)/ AEI/10.13039/501100011033;Comunidad de MadridInstituto de Salud Carlos III (ISCIII)/UEROBOTICSLABpu

Experimental Robot Model Adjustments Based on Force-Torque Sensor Information

The computational complexity of humanoid robot balance control is reduced through the application of simplified kinematics and dynamics models. However, these simplifications lead to the introduction of errors that add to other inherent electro-mechanic inaccuracies and affect the robotic system. Linear control systems deal with these inaccuracies if they operate around a specific working point but are less precise if they do not. This work presents a model improvement based on the Linear Inverted Pendulum Model (LIPM) to be applied in a non-linear control system. The aim is to minimize the control error and reduce robot oscillations for multiple working points. The new model, named the Dynamic LIPM (DLIPM), is used to plan the robot behavior with respect to changes in the balance status denoted by the zero moment point (ZMP). Thanks to the use of information from force-torque sensors, an experimental procedure has been applied to characterize the inaccuracies and introduce them into the new model. The experiments consist of balance perturbations similar to those of push-recovery trials, in which step-shaped ZMP variations are produced. The results show that the responses of the robot with respect to balance perturbations are more precise and the mechanical oscillations are reduced without comprising robot dynamicsThe research leading to these results received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

An inverse kinematics problem solver based on screw theory for manipulator arms

[Abstract] Several methodologies exist for solving the inverse kinematics of a manipulator arm. Basing on screw theory, it is possible to efficiently obtain complete and exact solutions. An open-source C++ implementation of an automated problem solver of this kind is introduced, and a comparative with selected known algorithms is established using the TEO humanoid robot platform by Universidad Carlos III de Madrid. The Orocos Kinematics and Dynamics Library is used for geometry and motion-related operationsThe research leading to these results has received funding from: European project “Human Centric Algebraic Machine Learning” (ALMA), H2020-EIC-FETPROACT-2019; ROBOASSET, “Sistemas robóticos inteligentes de diagnóstico y rehabilitación de terapias de miembro superior”, PID2020-113508RB-I00 funded by AGENCIA ESTATAL DE INVESTIGACION (AEI); RoboCity2030-DIHCM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by the European Social Funds (FSE) of the EU; the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR; and cofounded by Structural Funds of the EU.Comunidad de Madrid; S2018/NMT-433

ROS2 gesture classification pipeline towards gamified neuro-rehabilitation therapy

[Resumen] La rehabilitación es una herramienta esencial que ayuda a las personas a restaurar la movilidad en las extremidades afectadas por diversas afecciones, como enfermedades neurológicas. Las terapias convencionales, que incluyen terapia ocupacional, física y del habla, se han mejorado con nuevas tecnologías, como sistemas robóticos asistidos y juegos de realidad virtual y aumentada, para aumentar la participación y, en consecuencia, la efectividad. Esta investigación se centra en la implementación de un dispositivo portátil de sensores de electromiograma (EMG) de ocho canales, el brazalete Mindrove, para el reconocimiento de gestos. El objetivo es desarrollar un modelo clasificador utilizando el algoritmo de Máquinas de Vectores de Soporte (SVM) para distinguir ocho gestos diferentes de la mano y aplicarlo en un sistema de reconocimiento de gestos. El estudio demuestra la viabilidad de este sistema de reconocimiento y explora la aplicación potencial de esta tecnología en juegos interactivos de Unity para terapia de rehabilitación. Los resultados muestran una precisión prometedora en la clasificación del modelo y se necesita más investigación para abordar los desafíos relacionados con la especificidad del usuario y la precisión del reconocimiento de gestos. El trabajo futuro implica ampliar el repertorio de gestos reconocidos, incorporar datos adicionales del sensor y explorar técnicas de extracción de características más avanzadas para mejorar el rendimiento general del sistema de reconocimiento de gestos en terapias de rehabilitación.[Abstract] Rehabilitation is an essential tool that aids individuals in restoring mobility in limbs affected by various conditions, such as neurological diseases. Conventional therapies, including occupational, physical, and speech therapy, have been improved by new technologies, such as assistive robotic systems, along with virtual and augmented reality games, to enhance engagement and, consequently, effectiveness. This research focuses on implementing an eight-channel electromyogram (EMG) wearable sensor device, Mindrove armband, for gesture recognition. The objective is to develop a classifier model using the Support Vector Machine (SVM) algorithm to distinguish eight different hand gestures and apply it in a gesture recognition system. The study demonstrates the feasibility of this recognition system and explores the potential application of this technology in interactive Unity games for rehabilitation therapy. The results show promising accuracy in model classification, and further research is needed to address challenges related to user specificity and gesture recognition accuracy. Future work involves expanding the repertoire of recognized gestures, incorporating additional sensor data, and exploring more advanced feature extraction techniques to enhance the overall performance of the gesture recognition system in rehabilitation therapies.Ministerio de Ciencia e Innovación; PID2020-113508RBI0

An accessible interface for programming an assistive robot

In this paper, we present an accessible interface in the context of our work on bringing advanced robotics closer to everyday domestic users. This interface allows inexperienced users to be capable of programming an assistive robotic arm to perform a specific desired task in a household environment. The programming process is performed through the developed Web Browsable interface, within which a Task Creator Wizard plays an essential role. The robot's open architecture enables flexible multi-modal interaction. In addition to the touch buttons provided by the Web Browsable interface when presented on a touch screen, voice commands and the use of the Wii RemoteTM controller for intuitive robotic movement have also been enabled. The Web Browsable interface has been designed to provide high accessibility while taking aesthetic details into account, in order to prevent distraction caused by boredom of the user.Peer Reviewe

Impedance based Gym environment for the IIWA Collaborative robot towards human robot interaction

[Resumen] A medida que aumenta la fisioterapia de rehabilitación, también aumenta el deseo de soluciones robóticas robustas y adaptables para estandarizar y automatizar procedimientos comunes. Sin embargo, las técnicas de control modernas todavía tienen que migrar a entornos centrados en la interacción humana como los que requiere la fisioterapia. Esto se debe en gran parte a la falta de entornos de aprendizaje que puedan aprovechar los modernos robots con control de fuerzas. El objetivo de este artículo es introducir un nuevo entorno de aprendizaje por refuerzo (RL) para el entrenamiento de un manipulador robótico controlado por fuerza tanto en simulación como en el mundo real. Este problema puede dividirse en tres componentes, cada uno de los cuales depende del anterior. En primer lugar, se requiere un controlador de control robusto que sirva de puente entre el lenguaje de programación nativo de los robots (C++) y el lenguaje de programación Python, donde se implementan la mayoría de los algoritmos de RL. En segundo lugar, un controlador de impedancia cartesiana que pueda ajustarse para funcionar en la simulación y en el mundo real, abstrayendo la mayor parte de la complejidad de la de la dinámica de cuerpos rígidos. Y, por último, la construcción del entorno RL basado en la omnipresente interfaz OpenAI Gym.[Abstract] As rehabilitation physiotherapy is on the rise, so too is the desire for robust and adaptable robotic solutions to standarise and automate common procedures. Yet modern control techniques have still to migrate to human-interaction centered environments such as the ones required in physiotherapy. This is in great part due to the lack of learning environments that can leverage modern force controlled robots. The aim of this paper is therefore to introduce a new reinforcement learning (RL) environment for learning and training of a force controlled robotic manipulator both in simulation and in the real world. This problem can be divided into three components, each one depending on the last one. First, a robust control driver is required that bridges between the robots native programming language (C++) and the Python programming language, where most RL algorithms are implemented. Second, a cartesian impedance controller that can be fitted to work both in simulation and in the real world, abstracting away most of the complexity of rigid body dynamics. And finally the construction of the RL environment based on the ubiquitous OpenAI Gym interface.Esta investigación ha recibido fondos de los proyectos ROBOASSET PID2020-113508RBI00; ALMA H2020-EIC-FETPROACT-2019; proyecto de I+D+i PLEC2021-007819 financiado por MCIN/AEI/10.13039/501100011033 y por la Unión Europea NextGenerationEU/PRTR; RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiado por Programas de Actividades I+D en la Comunidad de MadridComunidad de Madrid; S2018/NMT-433

XGNITIVE: A Flexible Cognitive Architecture for Robots (Experiments)

<p>Dataset of experiments for XGNITIVE: A Flexible Cognitive Architecture for Robots. More information in its repository ( https://github.com/roboticslab-uc3m/xgnitive ).</p