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University of Waterloo

1. Wilde, Nils. Specifying User Preferences for Autonomous Robots through Interactive Learning.

Degree: 2020, University of Waterloo

This thesis studies a central problem in human-robot interaction (HRI): How can non-expert users specify complex behaviours for autonomous robots? A common technique for robot task specification that does not require expert knowledge is active preference learning. The desired behaviour of a robot is learned by iteratively presenting the user with alternative behaviours of the robot. The user then chooses the alternative they prefer. It is assumed that they make this decision based on an internal, hidden cost function. From the user's choice among the alternatives, the robot learns the hidden user cost function. We use an interactive framework allowing users to create robot task specifications. The behaviour of an autonomous robot can be specified by defining constraints on allowable robot states and actions. For instance, for a mobile robot a user can define traffic rules such as roads, slow zones or areas of avoidance. These constraints form the user-specified terms of the cost function. However, inexperienced users might be oblivious to the impact such constraints have on the robot task performance. Employing an active preference learning framework we present users with the behaviour of the robot following their specification, i.e., the constraints, together with an alternative behaviour where some constraints might be violated. A user cost function trades-off the importance of constraints and the performance of the robot. From the user feedback, the robot learns about the importance of constraints, i.e., parameters in the cost function. We first introduce an algorithm for specification revision that is based on a deterministic user model: We assume that the user always follows the proposed cost function. This allows for dividing the set of possible weights for the user constraints into infeasible and feasible weights whenever user feedback is obtained. In each iteration we present the path the user preferred previously again, together with an alternative path that is optimal for a weight that is feasible with respect to all previous iterations. This path is found with a local search, iterating over the feasible weights until a new path is found. As the number of paths is finite for any discrete motion planner, the algorithm is guaranteed to find the optimal solution within a finite number of iterations. Simulation results show that this approach is suitable to effectively revise user specifications within few iterations. The practicality of the framework is investigated in a user study. The algorithm is extended to learn about multiple tasks for the robot simultaneously, which allows for more realistic scenarios and another active learning component: The choice of task for which the user is presented with two alternative solutions. Through the study we show that nearly all users accept alternative solutions and thus obtain a revised specification through the learning process, leading to a substantial improvement in robot performance. Also, the users whose initial specifications had the largest impact on…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wilde, N. (2020). Specifying User Preferences for Autonomous Robots through Interactive Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16386

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wilde, Nils. “Specifying User Preferences for Autonomous Robots through Interactive Learning.” 2020. Thesis, University of Waterloo. Accessed October 23, 2020. http://hdl.handle.net/10012/16386.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wilde, Nils. “Specifying User Preferences for Autonomous Robots through Interactive Learning.” 2020. Web. 23 Oct 2020.

Vancouver:

Wilde N. Specifying User Preferences for Autonomous Robots through Interactive Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Oct 23]. Available from: http://hdl.handle.net/10012/16386.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wilde N. Specifying User Preferences for Autonomous Robots through Interactive Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16386

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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