The prompt and robust detection of tactile information is a relevant and challenging problem, and a considerable research effort is, thus, being put into innovative transduction methods for tactile sensors. In this article, we investigate the possibility of using event-based cameras to sense contact forces applied to objects by a robot end effector. The proposed optical tactile sensor incorporates a soft hemispherical pad made of silicone rubber with imprinted markers and a pulsewidth modulation light source that emits optical pulses, allowing robust detection of the markers to track deformations of the pad. To test the effectiveness of our sensor, experiments were carried out attaching it to a teleoperated robot arm to finely control it when out of the user's field of view, as accurately as if the user could see it. In the experiments, an augmented reality display and a haptic device were used to convey the force detected by the event-based tactile sensor back to the human operator. The experiments included a practical application of a soft tissue puncturing tool, and psychophysical test results from ten participants were recorded, to validate efficacy and usability of the system.

This paper studies the perception of robot intelligence for a manipulator that shares its workspace with a human operator, while the two execute independent tasks. Four different motion planning algorithms were employed to plan the robot motion in four subsequent experimental sets for 48 participants, and the order with which these algorithms were used was changed, obtaining all possible combinations. While guaranteeing safety, each of the four algorithms exhibited a different level of adaptability and reactivity, in terms of real-time motion planning abilities and of speed reduction based on heart rate feedback. We analyzed how perceived intelligence was influenced by the employed algorithm, by the order of execution and by the previous experience of the participants working with robots. In conclusion, perceived intelligence resulted being significantly lower in the first experimental set compared to the following ones, which showed a positive effect of habituation on perceived intelligence. On the other hand, neither the use of different motion planning algorithms nor the previous participants’ experience significantly influenced perceived intelligence.

This article proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and ofa posteriorievaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.