Authors: Ziang Liu, Yuanchen Ju, Yu Da, Tom Silver, Pranav N. Thakkar, Jenna Li, Justin Guo, Katherine Dimitropoulou, Tapomayukh Bhattacharjee
Abstract: Robot caregiving should be personalized to meet the diverse needs of care
recipients — assisting with tasks as needed, while taking user agency in
action into account. In physical tasks such as handover, bathing, dressing, and
rehabilitation, a key aspect of this diversity is the functional range of
motion (fROM), which can vary significantly between individuals. In this work,
we learn to predict personalized fROM as a way to generalize robot
decision-making in a wide range of caregiving tasks. We propose a novel
data-driven method for predicting personalized fROM using functional assessment
scores from occupational therapy. We develop a neural model that learns to
embed functional assessment scores into a latent representation of the user’s
physical function. The model is trained using motion capture data collected
from users with emulated mobility limitations. After training, the model
predicts personalized fROM for new users without motion capture. Through
simulated experiments and a real-robot user study, we show that the
personalized fROM predictions from our model enable the robot to provide
personalized and effective assistance while improving the user’s agency in
action. See our website for more visualizations:
https://emprise.cs.cornell.edu/grace/.
Source: http://arxiv.org/abs/2501.17855v1
