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 of human standard therapy for children with ASDs. We also discuss the next steps of developing RET robotic systems.

To investigate the effect of gestures in human–robot interaction, a number of social robots capable of gesturing have been designed. Gestures 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, they cannot be used for other robots. Therefore, when using a new robot platform with a different morphology, new joint trajectories to reach the desired postures need to be implemented. This method 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 gesture 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 database currently consists of a set of emotional expressions. The method was validated on the virtual model of different robots and an online survey was performed to evaluate the user’s perception of the output of the method. The results of this survey showed that the calculated gestures for a certain robot configuration well resemble the target gestures, and thus that our developed method to map gestures to different robot morphologies gives good results. Graphical Abstract

The assembly industry is shifting more towards customizable products, or requiring assembly of small batches. This requires a lot of reprogramming, which is expensive because a specialized engineer is required. It would be an improvement if untrained workers could help a cobot to learn an assembly sequence by giving advice. Learning an assembly sequence is a hard task for a cobot, because the solution space increases drastically when the complexity of the task increases. This work introduces a novel method where human knowledge is used to reduce this solution space, and as a result increases the learning speed. The method proposed is the IRL-PBRS method, which uses Interactive Reinforcement Learning (IRL) to learn from human advice in an interactive way, and uses Potential Based Reward Shaping (PBRS), in a simulated environment, to focus learning on a smaller part of the solution space. The method was compared in simulation to two other feedback strategies. The results show that IRL-PBRS converges more quickly to a valid assembly sequence policy and does this with the fewest human interactions. Finally, a use case is presented where participants were asked to program an assembly task. Here, the results show that IRL-PBRS learns quickly enough to keep up with advice given by a user, and is able to adapt online to a changing knowledge base.

One of the main restrictions of commercial cobots can be found in their limited payload to mass ratios. Flexible link manipulators seem to offer interesting advantages over traditional rigid robotics, in terms of lower self-weight, lower energy consumption and safer operation. However, the design and loading specifications in the general literature on flexible link manipulators differ from those expected in a collaborative industrial setting. In this paper, we want to investigate whether the use of flexible links can be truly beneficial for collaborative robotics. Firstly, the theoretical potential of flexible links to increase the payload to mass ratio is investigated. The feasibility to design a cylindrical flexible link for specific, realistic loading conditions is investigated, and the effect of link flexibility on the demanded motor torque and maximum reachable payload is visualised, while considering cylindrical links. Subsequently, to get insights in the accuracy and usability of such a manipulator, we experimentally quantify to what extent the undesired side effects of the flexible design can be counteracted using an appropriate controller. To comply with the envisioned application of collaborative robotics, a control strategy based on strain measurements along the link and robust to payload changes is proposed. The obtained accuracy was measured by tracking the end effector position using a Vicon motion capture system, considering two types of single link designs; firstly, a very flexible link setup with rectangular cross section, and secondly, a cylindrical flexible link, loaded to reach a payload to mass ratio of 1.