Note: This blog covers the work done by Duality's team at the ongoing Autodesk Research Residence Program. You can read the full article on the Autodesk Research Blog.
Deploying AI models in the real world requires precision, reliability, and safety. Whether identifying components on a production line or guiding an autonomous system, models must be trained on diverse, high-quality data to ensure robust performance. However, obtaining real-world training data at the necessary scale and variety is expensive, time-consuming, and often infeasible.
Digital twin simulation offers a scalable solution, rapidly generating vast amounts of synthetic training data under controlled and customizable conditions. At we developed Falcon, our digital twin simulation platform, to bridge the gap between AI training needs and real-world deployment. In this blog we dive into how digital twins are accelerating AI model development, and how our work in the Autodesk Research Residency Program is increasing accessibility to this approach.
Training AI Models with Digital Twins
Projects pioneering novel technologies thrive when industry partners are engaged to test and provide essential feedback. During our residency, Autodesk Research’s Robotics Team proved to be an ideal collaborator for exploring new digital twin methodologies. The team’s work with computer vision aided robotic assembly opened the door for exploring and comparing the efficacy of digital twins created through traditional (3D mesh) and novel (gaussian splatting) 3D reconstruction techniques. In collaboration with the team, we experimented with applying digital twin simulation to a robotic assembly task: assembling a skateboard using two six-degree-of-freedom (6DOF) robotic arms. To perform this task, the robot’s AI vision system needs to demonstrate reliable performance... [Read the full article on the Autodesk Research Blog]
Top: reference images of the Autodesk Research work environment. Bottom: digital twin of the environment used for some of the computer vision model training in collaboration with the Autodesk Research Robotics team.
