3HANDS Dataset: Learning from Humans for Generating Naturalistic Handovers with Supernumerary Robotic Limbs
Journal:
arXiv
Published Date:
Mar 6, 2025
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.
