3HANDS Dataset: Learning from Humans for Generating Naturalistic Handovers with Supernumerary Robotic Limbs

要旨

Supernumerary robotic limbs are robotic structures integrated closely with the user's body, which augment human physical capabilities and necessitate seamless, naturalistic human-machine interaction. For effective assistance in physical tasks, enabling SRLs to hand over objects to humans is crucial. Yet, designing heuristic-based policies for robots is time-consuming, difficult to generalize across tasks, and results in less human-like motion. When trained with proper datasets, generative models are powerful alternatives for creating naturalistic handover motions. We introduce 3HANDS, a novel dataset of object handover interactions between a participant performing a daily activity and another participant enacting a hip-mounted SRL in a naturalistic manner. 3HANDS captures the unique characteristics of SRL interactions: operating in intimate personal space with asymmetric object origins, implicit motion synchronization, and the user’s engagement in a primary task during the handover. To demonstrate the effectiveness of our dataset, we present three models: one that generates naturalistic handover trajectories, another that determines the appropriate handover endpoints, and a third that predicts the moment to initiate a handover. In a user study (N=10), we compare the handover interaction performed with our method compared to a baseline. The findings show that our method was perceived as significantly more natural, less physically demanding, and more comfortable.

著者
Artin Saberpour Abadian
Saarland University, Saarland Informatics Campus, Saarbrücken, Germany
Yi-Chi Liao
ETH Zürich, Zürich, Switzerland
Ata Otaran
Saarland Informatics Campus, Saarbrücken, Saarland, Germany
Rishabh Dabral
Max-Planck Institute for Informatics, Saarbrucken, Germany
Marie Muehlhaus
Saarland Informatics Campus, Saarbrücken, Germany
Christian Theobalt
Max Planck Institute for Informatics, Saarbrucken, Germany
Martin Schmitz
Saarland Informatics Campus, Saarbrücken, Germany
Jürgen Steimle
Saarland University, Saarland Informatics Campus, Saarbrücken, Germany
DOI

10.1145/3706598.3713306

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713306

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Interacting with Robots

G318+G319
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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