Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

49 Downloads (Pure)

Abstract

Enabling diverse users to program robots for different applications is critical for robots to be widely adopted. Most of the new collaborative robot manipulators come with intuitive programming interfaces that allow novice users to compose robot programs and tune their parameters. However, parameters like motion speeds or exerted forces cannot be easily demonstrated and often require manual tuning, resulting in a tedious trial-and-error process. To address this problem, we formulate tuning of one-dimensional parameters as an Active Learning problem where the learner iteratively refines its estimate of the feasible range of parameter values, by selecting informative queries. By executing the parametrized actions, the learner gathers the user's feedback, in the form of directional answers ("higher,'' "lower,'' or "fine''), and integrates it in the estimate. We propose an Active Learning approach based on Expected Divergence Maximization for this setting and compare it against two baselines with synthetic data. We further compare the approaches on a real-robot dataset obtained from programs written with a simple Domain-Specific Language for a robot arm and manually tuned by expert users (N=8) to perform four manipulation tasks. We evaluate the effectiveness and usability of our interactive tuning approach against manual tuning with a user study where novice users (N=8) tuned parameters of a human-robot hand-over program.
Original languageEnglish
Title of host publicationProceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, HRI'20
PublisherIEEE
Pages629–638
Number of pages10
ISBN (Electronic)978-1-4503-6746-2
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventACM/IEEE International Conference on Human-Robot Interaction - Cambridge, United Kingdom
Duration: 23 Mar 202026 Mar 2020
Conference number: 15
https://humanrobotinteraction.org/2020/

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE
ISSN (Print)2167-2121
ISSN (Electronic)2167-2148

Conference

ConferenceACM/IEEE International Conference on Human-Robot Interaction
Abbreviated titleHRI
CountryUnited Kingdom
CityCambridge
Period23/03/202026/03/2020
Internet address

Keywords

  • End-user programming
  • Human-robot interaction
  • Active Learning

Fingerprint Dive into the research topics of 'Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization'. Together they form a unique fingerprint.

  • Projects

    ROSE: Robots and the Future of Welfare Services

    Lundell, J., Brander, T., Kyrki, V., Racca, M. & Verdoja, F.

    01/01/201831/12/2021

    Project: Academy of Finland: Strategic research funding

    Cite this

    Racca, M., Kyrki, V., & Cakmak, M. (2020). Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, HRI'20 (pp. 629–638). (ACM/IEEE International Conference on Human-Robot Interaction). IEEE. https://doi.org/10.1145/3319502.3374784