Virtual worlds for solving real problems.
Falcon is a Digital Twin Simulation Platform trusted by leading robotics and AI/smart systems teams to help solve complex engineering problems. Users bring accurate digital twins of their environments and operating systems into Falcon where they can then generate high-fidelity data and predictive behavior modeling. This data enables users to develop and deploy automated systems robustly and at scale.
Falcon is the Digital Twin Simulation Platform helping transform the way users develop and deploy smart systems with domain tailored simulation designed to meet high-fidelity needs of any use case. Falcon is trusted by leading robotics and AI/smart systems teams as a complete spatial simulation solution that combines speed and accuracy with unparalleled accessibility. Accurate synthetic data and predictive behavior modeling generated in Falcon help our customers successfully bridge the Sim2Real gap.
Highlights
Sim2real: Tune system twins and virtual sensors to match observed data and proven data permeability from simulation output as verified by demanding Fortune 100 customers. Run Open-Loop simulation for synthetic data generation or Closed-Loop for autonomous systems validation and process automation - collaboratively and in the cloud.
Context: Quickly setup diverse scenarios using Unreal Engine based Editor and a massive catalog of simulation ready system, item and space twins.
Intelligence: Python API and Falcon middleware integrates with proprietary and open source messaging protocols like ROS/ROS2 and the latest generation of AI models.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay by the hour for the compute instance that runs the Falcon Digital Twin Simulator. The three options are GPU-backed instance sizes: g4dn.xlarge, g4dn.2xlarge, and g4dn.4xlarge. They differ by compute capacity, so you pick the size that matches your simulation workload. Billing is usage-based, charged per hour the instance runs. No upfront commitment applies. You scale cost up or down by choosing a larger or smaller instance and by how many hours you run it.
Top-of-mind questions for buyers
What GPU capacity do the g4dn.xlarge, g4dn.2xlarge, and g4dn.4xlarge instances give me?
Each option is a GPU-backed AWS instance. They scale up in CPU, memory, and GPU resources as you move from g4dn.xlarge to g4dn.2xlarge to g4dn.4xlarge. You pick the size that matches your simulation workload. Larger sizes handle bigger scenarios and more agents.
Am I charged when the instance is stopped or paused?
You pay by the hour only while the instance runs. Stopping the instance halts the hourly software charge. Powered-off instances may still incur underlying AWS storage fees for retained data, but the simulator meters running time only.
If I need more simulation power, do I switch instance sizes or run more instances?
You can move to a bigger instance size for a heavier single workload. You can also run more instances in parallel to handle multiple simulations at once. Both choices increase your total hourly cost, since each running instance meters separately.
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An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
SAR sensor v1.1
The SAR sensor has been updated to v1.1, adding orthorectification and energy-target output capabilities as specified by General Dynamics requirements.
Environment twins updated - Sedona, Downtown, Laurel
The Sedona, Downtown, and Laurel environment twins have been refreshed for the 6.1 build cycle with updated assets and lighting configurations.
Unreal Engine upgraded to 5.6
FalconSim 6.1 is built on Unreal Engine 5.6, bringing the latest rendering, physics, and tooling improvements from Epic Games.
IR sensor Phase 3 - material setup integrated into FalconEditor
PBIR Phase 3 material setup for environment twins is now integrated directly into FalconEditor, streamlining IR-ready environment twin creation. Note: environment twins from 5.x must be manually updated to conform to the new IR setup requirements.
Additional details
Usage instructions
Carefully review EC2 hourly prices and select the instance type that fits your budget and bandwidth requirements. We recommend g4dn.xlarge as Falcon requires a Tesla T4 GPU to operate.
Launch the instance with the desired instance type and connect to the instance by going to https://<instance ip address>:8443/#console . The instance is pre-configured with NiceDCV.
The initial login is "ubuntu" for the username and the instance ID for the password. Be sure to change the password to something only you know after logging in.
FalconSim and FalconEditor are installed and configured on your instance. You will find the readme.txt file on your desktop; this file contains basic operating instructions.
Remember to stop your instances when you don't need them to avoid EC2 hourly charges. Note that when you start your instance again, it will receive a new public IP address; you can use Elastic IP addresses to make your instances always have a permanent public IP address. As you need, create instances in multiple regions. Terminate instances when you no longer need them.
For any questions or queries regarding the usage of Falcon please refer to our tailored documentation with detailed examples to help you get started. If you still feel stuck please contact us at support+AWS@duality.ai with AWS Falcon YourSubject in the email title.
Our support team aims to respond within one business day. Expect a reply to the email address you used to send the query.
If you are keen on a support call or on demand training please use the support email ID.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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