Legible Grasping with Teleoperated Learning from Demonstration

Legible Grasping with Teleoperated Learning from Demonstration

van Beem, Marnix (TU Delft Mechanical, Maritime and Materials Engineering)

Babuska, R. (mentor)
Karageorgos, Dimitris (mentor)
Kober, J. (graduation committee)
Heemskerk, Cock (graduation committee)

Delft University of Technology

2022-06-17

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.

Learning from demonstration
Legibility
Teleoperation
Human-Robot Interaction

http://resolver.tudelft.nl/uuid:db4ca343-3713-43f5-ad16-de2fa27f4354

Student theses

master thesis

© 2022 Marnix van Beem