The general approach to generate collision free motion in a constraint environment is to use path planners, which demand a known environment and potentially fail otherwise. Learning from Demonstration (LfD) can be used instead to teach the robot unknown parts of the environment, such as a goal deviation or an unforeseen obstacle. The general approach is to train a model offline and expect it to perform well afterwards. Problems arise however when the model is trained insufficiently or unknown variations have occurred in the environment, which demand for refinement of the model. In online learning the operator is allowed to refine a predicted trajectory during execution time, where the state-of-the-art methods focus on kinaesthetic teaching. The contribution of this research is the development of a teleoperated online learningmmethod, where the operator can make refinements by moving a haptic stylus (Phantom Omni) in the desired direction. By doing this, a force is felt proportional to the magnitude of refinement. After creating such a refined trajectory, it is used to update a condition dependent probabilistic trajectory model. The proof of concept was shown on a 2D example and on a simulated robot that shows that we can adapt an initial model when unknown variations occur and that the method is able to deal with different object positions and initial end effector poses. To show if other people can use the method, a human factors experiment is performed, comparing the developed method against three other methods on how much time it takes to successfully adapt a model (refinement time) and on the perceived workload. Two different parameters are varied, which are the teaching device (stylus or keyboard) and the learning mechanism (online or offline). The expectation was that both online with stylus has the lowest refinement time and workload, but the results show that only online has a significant improvement over offline methods (p = 7.94 × 10^−12 and p = 0.000512 respectively). This is explained by the fact that only small corrections have to be made and in a maximum of three degrees of freedom (DoFs). No significant difference was found between keyboard and stylus (p = 0.755 and p = 0.302 respectively). An explanation for this is that this is task, person and implementation dependent. The recommendations are to evaluate the proof of concept on the real robot and to extend the method with orientation refinement, such that more complex tasks can also be dealtwith. We hypothesize that with these tasks the combination of online with stylus does perform the best.

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.

This thesis proposes the novel Behaviour Tree Update Framework (BTUF) for the initial construction and continuous incremental adaptation of Behaviour Trees (BTs) for applications in Learning from Demonstration (LfD) frameworks to create complex robot behaviours associated with Activities of Daily Living (ADL) without requiring the user to have a programming or engineering background. BTUF implements several methods towards that end. Automatic generation of the fundamental structure of BTs within BTUF allows for easy human operation of the framework's Text-based User Interface (TUI). Saving and loading of constructed trees facilitates easy expansion and reusability of constructed trees. By expanding upon an initial base behaviour, seemingly simple behaviours can be adapted to facilitate novel instances thereupon, increasing the complexity and functionality of the constructed tree over time. Experimental validation in the form of a user study has provided proof-of-concept within simulation and has given insight in the initial overall performance and general system acceptance of BTUF. Future work is recommended for further validation and improvement of the proposed framework to one day realise a real-life application within healthcare robotics.