Personalized Meta-Learning for Domain Agnostic Learning from Demonstration

Schrum, Mariah L.; Hedlund-Botti, Erin; Gombolay, Matthew

doi:10.1109/hri53351.2022.9889377

Cited by 1 publication

(1 citation statement)

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“…They then gathered a new round of user data in order to determine the mapping between user motions and the previously identified latent variables. Importantly, these control schemes were personalized to every user, and other papers support the premise that that some level of control scheme personalization is required in order to accommodate participants’ individual, internal models ( Paleja and Gombolay, 2019 ; Schrum et al, 2022 ; Bobu et al, 2023 ).…”

Section: Related Workmentioning

confidence: 99%

Clustering user preferences for personalized teleoperation control schemes via trajectory similarity analysis

Molnar,

Agrawal,

Chernova

2024

Front. Robot. AI

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Successful operation of a teleoperated robot depends on a well-designed control scheme to translate human motion into robot motion; however, a single control scheme may not be suitable for all users. On the other hand, individual personalization of control schemes may be infeasible for designers to produce. In this paper, we present a method by which users may be classified into groups with mutually compatible control scheme preferences. Users are asked to demonstrate freehand motions to control a simulated robot in a virtual reality environment. Hand pose data is captured and compared with other users using SLAM trajectory similarity analysis techniques. The resulting pairwise trajectory error metrics are used to cluster participants based on their control motions, without foreknowledge of the number or types of control scheme preferences that may exist. The clusters identified for two different robots shows that a small number of clusters form stably for each case, each with its own control scheme paradigm. Survey data from participants validates that the clusters identified through this method correspond to the participants’ control scheme rationales, and also identify nuances in participant control scheme descriptions that may not be obvious to designers relying only on participant explanations of their preferences.

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Section: Related Workmentioning

confidence: 99%