Preference-Based Optimization of a Human-Robot Collaborative Controller. Issue 38 (2022)

• Record Type: Journal Article
• Authors: Maccarini, Marco; Pura, Filippo; Piga, Dario; Roveda, Loris; Mantovani, Lorenzo; Braghin, Francesco
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 38(2022)
• Page Start: 7
• Page End: 12
• Publication Date: 2022
• Subjects: global optimization -- preference-based optimization -- surrogate-based methods -- active preference learning -- human-robot collaboration -- control tuning; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.126
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Preference-based optimization is a powerful tool to improve the performance of a system in an intuitive way. Such a methodology allows for solving optimization problems in which the decision-maker cannot evaluate the objective function related to the target problem, but rather can only express a preference such as "this is better than that" between two candidate decision vectors. In such a way, the target cost function can be easily defined and implemented, avoiding complex formulations and the use of external sources of data. Such a methodology is nowadays deeply investigated to reduce the complexity related to systems/applications tuning in real applications, providing the user with a natural procedure capable to exploit his/her knowledge and preferences. Physical human-robot collaboration (HRC) is an important field of application considering the preference-based optimization topic. Physical HRC is increasingly important in many different domains. Properly tuning the robot behavior is important to achieve satisfactory performance from the human's perspective. However, a common tuning for all the possible subjects is not feasible due to their different skills, different backgrounds, and different expected performance. However, ad hoc tuning of the robot behavior is not trivial. To address this issue, the here presented contribution applies preference-based optimization to the tuning of a physical HRC controller. In such a way, an ad hoc tuning of the robot behavior based on the perceived interaction is achieved for each subject. Experimental results show the capabilities of the method, being able to optimize the robot behavior in limited optimization trials.