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

• URL: http://arxiv.org/abs/2503.04635v1
• Date: Thu, 06 Mar 2025 17:23:55 GMT
• Title: 3HANDS Dataset: Learning from Humans for Generating Naturalistic Handovers with Supernumerary Robotic Limbs
• Authors: Artin Saberpour Abadian, Yi-Chi Liao, Ata Otaran, Rishabh Dabral, Marie Muehlhaus, Christian Theobalt, Martin Schmitz, Jürgen Steimle,
• Abstract summary: Supernumerary robotic limbs (SRLs) are robotic structures integrated closely with the user's body.<n>We present 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.<n>We present three models that generate naturalistic handover trajectories, one that determines the appropriate handover endpoints, and a third that predicts the moment to initiate a handover.
• Score: 64.99122701615151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Supernumerary robotic limbs (SRLs) 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.

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