Online Human Activity Recognition for Ergonomics Assessment

International audienc

Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization

Multi-task prioritized controllers are able to generate complex robot behaviors that concurrently satisfy several tasks and constraints. To perform, they often require a human expert to define the evolution of the task priorities in time. In a previous paper [1] we proposed a framework to automatically learn the task priorities thanks to a stochastic optimization algorithm (CMA-ES) maximizing the robot performance on a certain behavior. Here, we learn the task priorities that maximize the robot performance, ensuring that the optimized priorities lead to safe behaviors that never violate any of the robot and problem constraints. We compare three constrained variants of CMA-ES on several benchmarks, among which two are new robotics benchmarks of our design using the KUKA LWR. We retain (1+1)-CMA-ES with covariance constrained adaptation [2] as the best candidate to solve our problems, and we show its effectiveness on two whole-body experiments with the iCub humanoid robot

Online Human Activity Recognition for Ergonomics Assessment

International audienceWe address the problem of recognizing the current activity performed by a human worker, providing an information useful for automatic ergonomic evaluation of workstations for industrial applications.Traditional ergonomic assessment methods rely on pen-and-paper worksheet, such as the Er-gonomic Assessment Worksheet (EAWS). Nowadays, there exists no tool to automatically estimate the ergonomics score from sensors (external cameras or wearable sensors). As the ergonomic evaluation depends of the activity that is being performed, the first step towards a fully automatic ergonomic assessment is to automatically identify the different activities within an industrial task. To address this problem, we propose a method based on wearable sensors and supervised learning based on Hidden Markov Model (HMM). The activity recognition module works in two steps. First, the parameters of the model are learned offline from observation based on both sensors, then in a second stage, the model can be used to recognize the activity offline and online. We apply our method to recognize the current activity of a worker during a series of tasks typical of the manufacturing industry. We recorded 6 participants performing a sequence of tasks using wearable sensors.Two systems were used: the MVN Link suit from Xsens and the e-glove from Emphasis Telematics (See Fig. 1). The first consists of 17 wireless inertial sensors embedded in a lycra suit, and is used to track the whole-body motion. The second is a glove that includes pressure sensors on fingertips, and finger flexion sensors. The motion capture data are combined with the one from the glove and fed to our activity recognition model. The tasks were designed to involve elements of EAWS such as load handling, screwing and manipulating objects while in different static postures. The data are labeled following the EAWS categories such as " standing bent forward " , " overhead work " or " kneeling ". In terms of performances, the model is able to recognize the activities related to EAWS with 91% of precision by using a small subset of features such as the vertical position of the center of mass, the velocity of the center of mass and the angle of the L5S1 joint

Chapter 35: Free Simulation Software and Library

International audienceWith the advent of powerful computation technologies and efficient algorithms , simulators became an important tool in most engineering areas. The field of humanoid robotics is no exception; there have been numerous simulation tools developed over the last two decades to foster research and development activities. With this in mind, this chapter is written to introduce and discuss the current-day open source simulators that are actively used in the field. Using a developer-based feedback, we provide an outline regarding the specific features and capabilities of the open-source simulators, with a special emphasis on how they correspond to recent research trends in humanoid robotics. The discussion is centered around the contemporary requirements in humanoid simulation technologies with regards to future of the field

Introduction to the Special Theme on Human-Robot Interaction

International audienceThis special issue addresses the state of the art of human-robot interaction (HRI), discussing the current challenges faced by the research community for integrating both physical and social interaction skills into current and future collaborative robots

Developmental object learning through manipulation and human demonstration

International audienceWe present a cognitive developmental approach for a humanoid robot exploring its close environment in an interactive scenario, taking inspiration from the way infants learn about objects. The proposed approach allows to detect physical entities in the visual space, to create multi-view appearance models of these entities and to categorize them into robot parts, human parts and manipulated objects without supervision and without prior knowledge about their appearances. All information about the entities appearances and behaviour is incrementally acquired while the robot and its human partner interact with objects