Collaborative robots can help industry workers to improve their ergonomics. They can propose a safe and ergonomic posture to the workers to reduce the risk of musculoskeletal disorders. Proposing an ergonomic stance needs postural evaluation and optimization. To optimize the workers' posture, we need to run the optimization on a cost function representing the ergonomic status. The tabular ergonomic assessment methods are the most common methods used by ergonomists, but they are linear stepwise functions that are not differentiable and not suitable for optimization purposes. We propose NeuroErgo, a deep neural network model that can approximate the tabular ergonomic assessment methods more precisely than existing methods. By solving the task constraints optimization problem for any task in industry and NeuroErgo as posture cost function, a safe and ergonomic posture can be derived and recommended to the workers while accomplishing their job.

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.

Many robots capable of performing social behaviors have recently been developed for Human-Robot Interaction (HRI) studies. These social robots are applied in various domains such as education, entertainment, medicine, and collaboration. Besides the undisputed advantages, a major difficulty in HRI studies with social robots is that the robot platforms are typically expensive and/or not open-source. It burdens researchers to broaden experiments to a larger scale or apply study results in practice. This paper describes a method to modify My Keepon, a toy version of Keepon robot, to be a programmable platform for HRI studies, especially for robot-assisted therapies. With an Arduino microcontroller board and an open-source Microsoft Visual C# software, users are able to fully control the sounds and motions of My Keepon, and configure the robot to the needs of their research. Peripherals can be added for advanced studies (e.g., mouse, keyboard, buttons, PlayStation2 console, Emotiv neuroheadset, Kinect). Our psychological experiment results show that My Keepon modification is a useful and low-cost platform for several HRI studies.