Radeon-hackathon-2026-07

how to apply and use AMD Radeon GPU

see README

Track 3 starter demo: robot simulation on AMD Radeon GPU

New to robotics, or want to learn how to run robot simulation on AMD GPUs? This reference demo is a quick, hands-on starting point for Track 3 participants — an end-to-end pipeline where a Franka Panda arm picks fruit off a table and places it in a bowl, built on the Genesis physics engine and LeRobot, running on an AMD Radeon (ROCm) GPU.

▶️ Demo repo & videos: https://github.com/wangxunx/franka_fruit_pick_demo

What you'll learn:

• Set up a robot simulation environment on an AMD Radeon GPU (ROCm), using the prebuilt ROCm PyTorch wheels
• Build a scene and run physics simulation with Genesis
• Record data, apply domain randomization, and train a visuomotor policy with LeRobot
• Go end-to-end — from a scripted pick-and-place to a trained, closed-loop policy, with evaluation videos

Note: this is a learning reference to show how to run simulation and training on an AMD GPU with genesis-world + lerobot; the trained model's success rate is not guaranteed.

when you submit

pls fork this repo and open a pull request including the stuff that is mentioned in Rules&conditions of luma page. the title of pull request should be like "Track x, Team name, your application name"

Important

Team name was an optional field on the Luma registration form. If you did not fill in a team name when you registered, please use your own name instead, so the title of the pull request should be like "Track x, Your name, your application name".

Note

All submission materials, project descriptions, and Pull Requests should be submitted in English.

Submission Requirements

Track 1: Development of Multimodal Content Creation Tools

1. Project Profile Document (PDF)
   • Project background
   • Target users & application scenarios
   • System architecture
   • Model & algorithm introduction
   • Adaptation description for AMD Radeon GPU / ROCm
2. Project Source Code
   • Complete source code repository
   • README file including environment configuration, startup guide and dependency list
3. Demo Video
   • Recommended duration: 3–5 minutes
   • Demonstrate the actual operation process
   • The actual execution performance on an AMD Radeon GPU, from command line/GUI to the final result (clarity, stability and diversity of outputs)
4. Supplementary Materials (Choose One)
   • PPT / Poster (highlight creative scenarios, practical value of the tool)

Track 2: Development & Local Deployment of Private AI Agents

1. Project Specification Document
   • Application scenarios
   • Agent architecture diagram
   • Introduction to core capabilities
   • Model introduction & local deployment plan
   • Optimization description for inference speed on AMD Radeon GPU
2. Project Source Code
   • Complete source code repository
   • README file including environment configuration, startup guide and dependency list
3. Demo Video
   • Recommended duration: 3–5 minutes
   • Demonstrate the actual operation process
   • The actual execution performance on an AMD Radeon GPU, from command line/GUI to the final result (fluidity and functional completeness)
4. Supplementary Materials (Choose One)
   • PPT / Poster

Track 3: Physical AI Challenge – Robotics Simulation and Application Design based on AMD Radeon GPUs and ROCm

1. Technical Report (should include, but is not limited to):
   • Definition and description of the target application
   • Overall system architecture and solution design
   • Description of the datasets used for training and/or evaluation
   • Explanation of how AMD Radeon GPUs are utilized during training, inference, and other relevant stages
   • Description of the innovations, key technical contributions, and important aspects of the project
   • Description of the final deliverables and output forms of the project
   • Any additional information that participants believe highlights the strengths or unique aspects of their work
   • Introduction of team members and their respective contributions
2. Project Source Code
   • Dedicated source code repositories
   • A Docker image containing the complete source code and all required components for running the project would be preferable
3. Reproducibility Instruction README — a detailed README document containing:
   • Environment setup instructions
   • Execution and usage instructions
   • Dependency specifications
   • Step-by-step reproduction procedures
   • Following the provided instructions should allow evaluators to reproduce the submitted results
4. Demonstration Video (Recommended Length 3~5 minutes)
   • The video should demonstrate the complete workflow of the project, including command-line and/or GUI operations, execution procedures, and results
5. Supplementary materials in other formats may be submitted to demonstrate the value of the proposed technical solution.