Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

15 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, HRI'20
KustantajaIEEE
Sivut629–638
Sivumäärä10
ISBN (elektroninen)978-1-4503-6746-2
DOI - pysyväislinkit
TilaJulkaistu - 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaACM/IEEE International Conference on Human-Robot Interaction - Cambridge, Iso-Britannia
Kesto: 23 maaliskuuta 202026 maaliskuuta 2020
Konferenssinumero: 15
https://humanrobotinteraction.org/2020/

Julkaisusarja

NimiACM/IEEE International Conference on Human-Robot Interaction
KustantajaIEEE
ISSN (painettu)2167-2121
ISSN (elektroninen)2167-2148

Conference

ConferenceACM/IEEE International Conference on Human-Robot Interaction
LyhennettäHRI
MaaIso-Britannia
KaupunkiCambridge
Ajanjakso23/03/202026/03/2020
www-osoite

Sormenjälki Sukella tutkimusaiheisiin 'Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

  • Projektit

    ROSE: Robotit ja hyvinvointipalvelujen tulevaisuus

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

    01/01/201830/06/2021

    Projekti: Academy of Finland: Strategic research funding

    Siteeraa tätä

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