Mechanisms and Computational Design of Multi-Modal End-Effector with Force Sensing using Gated Networks

TL;DR

This paper presents MAGPIE, a multi-modal robotic end-effector with force sensing that can switch between configurations, integrating sensors and computational models to enhance dual-functionality in limbed robots.

Contribution

It introduces a novel multi-modal end-effector with integrated force sensing and a computational design framework for noise-robust sensing mechanisms.

Findings

Successful hardware validation of MAGPIE as a foot and gripper

Effective force sensing with hall effect sensors

Validated computational models for sensing and control

Abstract

In limbed robotics, end-effectors must serve dual functions, such as both feet for locomotion and grippers for grasping, which presents design challenges. This paper introduces a multi-modal end-effector capable of transitioning between flat and line foot configurations while providing grasping capabilities. MAGPIE integrates 8-axis force sensing using proposed mechanisms with hall effect sensors, enabling both contact and tactile force measurements. We present a computational design framework for our sensing mechanism that accounts for noise and interference, allowing for desired sensitivity and force ranges and generating ideal inverse models. The hardware implementation of MAGPIE is validated through experiments, demonstrating its capability as a foot and verifying the performance of the sensing mechanisms, ideal models, and gated network-based models.