“Workmates of Indumental, Gaiker and Tecnalia interacted with a computer simulation of a manufacturing machine”
Nowadays, in industrial environments the trend is to achieve hybrid human-robot collaboration by replacing multiple currently manual, expensive and time-consuming tasks with correspondingly automatic robotic-based procedures.
More specifically, the goal of HR-Recycler project is to create a hybrid collaborative environment, where humans and robots will share and undertake, at the same time, different processing and manipulation tasks, targeting the industrial application case of WEEE recycling.
However, in order to achieve a successful interaction, a great level of trust is required between human and machine. Therefore, our project considers human factors and social cognition as key components to evaluate robot’s behaviour in terms of trust.
Within the scope of this project, TECNALIA is working in a Human-Robot trust classification model development that will be trained using inputs of psychophysiological signals from Human-Robot interactive collaboration.
Normally, in industrial environments is quite unfriendly to test these type of developments (due to complexity and quantity of items of Personal Protection Equipment used). So, in this research project, the trust classifier will be validated in a realistic 3D Virtual Reality industrial environment, which will be implemented ad hoc for this purpose.
The novelty of this project is the advance in the inference of human trust when interacting with a robot co-worker, in terms of including new psychophysiological signals as respiration and eye-tracker in the development of a trust classifier based on machine learning and the validation in an ad hoc 3D Virtual Reality environment that requires user interaction and physical movements that may generate noise on the desired signals.
The main objective is to detect robust psycho-physiological signals to discriminate between trust and distrust situations in human-robot interaction. This will enable to design machines capable to respond to changes in human trust level and rebalance the robot’s behaviour when a low confidence level is detected.
To achieve this objective, TECNALIA will work in a doble-stage empirical procedure.
The first experiment held on last week with the collaboration of 60 workmates of Indumental, Gaiker and Tecnalia. In this experiment, each person interacted with a computer simulation of a manufacturing machine. Therefore, each trial presented a stimulus (sensor reading), a response (participant’s choice) and an outcome (machine working or not). This studio was a laboratory based and several psychophysiological signals were captured while forcing trust/distrust situations. So, this will allow us to model a Human trust classifier according to the most significant signals.
The second experiment will use a VR environment to recreate real working conditions and validate the previous analytical results. Using a virtual environment allows us to provoke some validating conditions (for instance, robot malfunction) without running unnecessary health risks.
The RV experiment will replicate the exact layout of a Human-Robot collaborative workstation of a manufacturing plant. The participants will be asked to perform the same activities as the workers on the real plant and the virtual robots will interact with them on the same reality basics. However, during the experiments, sometimes robots will malfunction (will have longer waiting time or they will move more abruptly), thus compromising the human trust.
