State-of-the-art object grasping with 7-DOF robotic manipulators requires joint configuration planning methods in order to provide position control of the end-effector. These motion planners are able to calculate a motion plan to execute a safe grasp, while taking environmental constraints into account. In human-robot interaction, a well known problem is that humans are uneasy with the arm motion the robot executes, because the motion plan lacks parametrization of variables which would account for the impression of legibility. In this study we develop a method which allows for teleoperated learning from a single demonstration that is perceived more legible by humans. The operator uses the Geomagic Touch haptic device to demonstrate a movement of the robot’s end-effector. Modeling a motion path from a single teleoperated demonstration is achieved using Dynamic Movement Primitives. The effectiveness of the teleoperated LfD module has been demonstrated both in simulation and on a TIAGo robot in a variety of poses. An experiment is conducted in which a state-of-the-art motion planner was compared to the proposed LfD method and the ability of human participants to predict the goal object of the robot. Using the teleoperated LfD method, the ability to predict the goal objects increases significantly and the human is more confident in making the prediction (P = 0.0102 and P < 0.001, respectively). This means that with the learning method a more legible grasp was generated than with the state-of-the-art motion planner.

Facial expression recognition today is a widely-researched topic with some pertinent applications in human-computer interaction (HCI), surveillance, etc. The diverse range of expressions makes data collection expensive in terms of time and money, making task-specific data collection inconvenient. This thesis project investigates a possible method for quickly and cheaply collecting this data, especially for a Human Computer Interaction (HCI) application. In particular, the focus is on extit{Pepper}, a robot meant for interacting with humans through conversation. For collecting such data, emotions should be triggered in participants and their faces should be video-recorded. The triggering method used was to show videos selected for triggering specific emotions, letting participants watch them in pairs, thus enabling mutual interactions to better simulate the social environment in which the robot will operate. After watching each video, participants were asked to rate their feelings through the extit{AffectButton}, a tool for intuitively describing emotions in a dimensional way. Video selection was based on a questionnaire in which people were asked to rate emotions a video was triggering in them. Recordings obtained from people watching the triggering video were then compared to a model of a neutral expression performed by the same participant, in order to select the frames in which expressions were shown, ignoring neutral and transition frames. The pictures obtained were included in a dataset, which is used for training a linear regressor and a Convolutional Neural Network (CNN). These were then tested on naturalistic data taken during conversations, in order to investigate whether the proposed data collection method could build a useful dataset and the results showed this method to be promising.