Open Arms: Open-Source Arms, Hands & Control

TL;DR

Open Arms is an open-source, low-cost robotic hand and arm platform with advanced control, sensing, and simulation tools, designed for humanoid robotics and prosthetics, demonstrating high dexterity and grasping accuracy.

Contribution

This work introduces a modular, open-source robotic hand and arm framework with a comprehensive SDK, simulation, and control system, including a novel grasping CNN achieving state-of-the-art accuracy.

Findings

Achieved 92.4% accuracy on Cornell Grasping Dataset.

Demonstrated real-time grasping at 22ms per inference.

Developed a low-cost, high-functionality robotic hardware and software platform.

Abstract

Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have designed and established the manufacturing of Open Arms as a low-cost, high functionality robotic arms hardware and software framework to serve both humanoid robot applications and the urgent demand for low-cost prosthetics, as part of the Hanson Robotics Sophia Robot platform. Using the techniques of consumer product manufacturing, we set out to define modular, low-cost techniques for approximating the dexterity and sensitivity of human hands. To demonstrate the dexterity and control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN) model that can generate robust antipodal grasps from input images of various objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of 92.4% using our model architecture on a standard Cornell Grasping Dataset, which contains a diverse set of household objects.