Simulating Safe Bite Transfer in Robot-Assisted Feeding with a Soft Head and Articulated Jaw
Yi Heng San, Vasanthamaran Ravichandram, J-Anne Yow, Sherwin Stephen Chan, Yifan Wang, Wei Tech Ang
TL;DR
The paper tackles safe bite transfer in robot-assisted feeding by introducing a MuJoCo-based framework that models a soft-body head tightly coupled to a rigid skull via tendon-driven skinning. It reconstructs a deformable head from an RGB image using DECA, converts it to a tetrahedral mesh, and enables mandible actuation to simulate mouth opening. Bite transfer is parameterized by insertion depth $d$, entry angle $\alpha$, exit depth $e$, and exit angle $\beta$, allowing systematic exploration of contact forces $f_t$ to optimize user comfort. The main finding is that a straight-in, straight-out strategy with $\alpha = 90^{\circ}$, $d = 70\,\mathrm{mm}$, $\beta = 90^{\circ}$, $e = 10\,\mathrm{mm}$ minimizes both total and peak contact forces, suggesting practical guidelines for safe robot-assisted feeding; the work enables safer, simulation-based evaluation before real-world trials.
Abstract
Ensuring safe and comfortable bite transfer during robot-assisted feeding is challenging due to the close physical human-robot interaction required. This paper presents a novel approach to modeling physical human-robot interaction in a physics-based simulator (MuJoCo) using soft-body dynamics. We integrate a flexible head model with a rigid skeleton while accounting for internal dynamics, enabling the flexible model to be actuated by the skeleton. Incorporating realistic soft-skin contact dynamics in simulation allows for systematically evaluating bite transfer parameters, such as insertion depth and entry angle, and their impact on user safety and comfort. Our findings suggest that a straight-in-straight-out strategy minimizes forces and enhances user comfort in robot-assisted feeding, assuming a static head. This simulation-based approach offers a safer and more controlled alternative to real-world experimentation. Supplementary videos can be found at: https://tinyurl.com/224yh2kx.
