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Build an ML inference library to interpret human hand gestures
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Build a computer UI for gameplay
- 3..2..1..go
- Visual display of computer move
- Reset game
- Humans play multiple rounds (i.e. best of 10)
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Build a humanoid robotic arm to show computer moves
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All gameplay components run in containers
- camera feed (rtsp feed preferred to onboard integration)
- Inference algorithm
- Robot arm gestures
- Gameplay UI
- Message bus
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Deliver the solution as an open source project in a public git repo
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Demo the cluster(s) connected via Rancher
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Demo gitops updates/rollbacks for various components
- Jetson Xavier NX Dev Kit
- Raspberry Pi camera (alternative streaming webcam)
- Robotic Arm (https://github.com/mak3r/edge-robot-demo)
- Rancher Management Server (running where?)
- Build a secondary library to do predictive gameplay for the computer player
- Receive camera feed from webcam interfaces remotely
- Akri integration for camera, robot, jetson
The “demo” is broken into two basic parts. Part 1 is an interactive game of Rock Paper Scissors in which human players attempt to win against the computer. Part 2 will be a presentation that includes both gameplay and the unpacking of NUCs prepared for the OT environment and dropped in to demonstrate how AI/ML, Edge systems and Secure Device Onboarding can be integrated with cloud native technologies to provide complete, secure and remotely manageable end-to-end solutions.
The interactive demo will have 5-6 physical components on the desktop. These include:
- 3 NUCs as the Kubernetes cluster
- A web cam for input of human player gestures
- A robotic arm for expression of the computer gestures
- A device to start/stop/reset the game