Open Arms: Open-Source Arms, Hands & Control
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 20 May 2022.
Builds on
7 of 21 references.
References · 7
- 2018Learning Dexterous In-Hand Manipulationcited by 643
- 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
- 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 208
- 2013Deep Learning for Detecting Robotic Graspscited by 114
- 2014Real-Time Grasp Detection Using Convolutional Neural Networkscited by 75
- 2016Robotic Grasp Detection using Deep Convolutional Neural Networkscited by 47
- 2018Fully Convolutional Grasp Detection Network with Oriented Anchor Boxcited by 27
Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024Leveraging Large Language Models in Human-Robot Interaction: A Critical Analysis of Potential and Pitfallscited by 5
- 2024Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Controlcited by 2
- 2025Sentience Quest: Towards Embodied, Emotionally Adaptive, Self-Evolving, Ethically Aligned Artificial General Intelligencecited by 0
- 2025Sentience Quest: Towards Embodied, Emotionally Adaptive, Self-Evolving, Ethically Aligned Artificial General Intelligence18 May 2025
- 2024Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control12 Jun 2024
- 2024Leveraging Large Language Models in Human-Robot Interaction: A Critical Analysis of Potential and Pitfalls26 Mar 2024