Search

Collaborative robots or cobots are gaining ground in the manufacturing industry. Unlike classical robot, they are more flexible and open the door to a large panel of applications whereby the human and the robot’s skills are combined to achieve tasks jointly. Cobots can also help decreasing the workload of the operator. Nowadays, musculoskeletal disorders (MSDs) represent the main cause of absenteeism at work. This is accentuated by the ageing of the population. Thanks to the development of human tracking systems, it is possible to monitor the operator in his workstation and analyze his postures. Using ergonomic measures such as the Rapid Entire Body Assessment (REBA) method, the risk related to a bad posture can be determined. In this paper, we present an algorithm to compute ergonomic postures based on the object/tool pose to be manipulated. Optimal postures are determined using an inverse kinematics method and the data returned by the ergonomics monitoring system.

Robot-assisted therapy (RAT) offers potential advantages for improving the social skills of children with autism spectrum disorders (ASDs). This article provides an overview of the developed technology and clinical results of the EC-FP7- funded Development of Robot-Enhanced therapy for children with AutisM spectrum disorders (DREAM) project, which aims to develop the next level of RAT in both clinical and technological perspectives, commonly referred to as robot-enhanced therapy (RET). Within this project, a supervised autonomous robotic system is collaboratively developed by an interdisciplinary consortium including psychotherapists, cognitive scientists, roboticists, computer scientists, and ethicists, which allows robot control to exceed classical remote control methods, e.g., Wizard of Oz (WoZ), while ensuring safe and ethical robot behavior. Rigorous clinical studies are conducted to validate the efficacy of RET. Current results indicate that RET can obtain an equivalent performance compared to that ofhuman standard therapy for children with ASDs. We also discuss the next steps of developing RET robotic systems.

Collaborative robots (cobots) have a strong potential to improve both productivity as well as the working conditions of assembly operators by assisting in their tasks and by decreasing their physical and cognitive stress. The use of cobots in factories however introduces multiple challenges: how should the overall assembly architecture look like? How to allocate specific (sub)tasks to the operator or the cobot? How to program and deploy the cobot? How to make changes to the robot program? In this paper dilogy, we briefly highlight our recent contributions to this field. In part I we presented our collaborative architecture for human-robot assembly tasks and discussed the working principles of our task allocation framework, based upon agent capabilities and ergonomic measurements. In this second part we focus on our programming by demonstration approach targeted at expediting the deployment of learnable robot skills.

Gestures for social robots are often preprogrammed off-line or generated by mapping motion capture data to the robot. Since these gestures are dependent on the robot's joint configuration, new joint trajectories to reach the desired postures need to be implemented when using a new robot platform with a different morphology. The method proposed here aims to minimize the workload when implementing gestures on a new robot platform and facilitate the sharing of gestures between different robots. The innovative aspect of this method is that it is constructed independently of any robot configuration, and therefore it can be used to generate gestures for different robot platforms. To calculate a posture for a certain configuration, the developed method uses a set of target gestures listed in a database and maps them to that specific configuration. The method was validated on a series of configurations, including those of existing robots.

No abstract

Gestures and body language are important non-verbal modalities in human communication. Therefore, the implementation of gestures and body language in robots also benefits the interaction between humans and robots. In contrast to facial expressions, no generic method for the taxonomy and generation of gestures exists. The gestures implemented in the current state of the art robots are always limited, often preprogrammed offline and dependent on the morphology or generated by human motion tracking. This paper describes the development of a generic method for the generation of gestures by social robots.