Physical AI, Simulation & Intelligent Machines
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Curated Certification CHR09
Integrate simulation, perception, planning, controls and learning into intelligent machines using digital twins, synthetic data, robot learning, scalable evaluation and explicit sim-to-real risk management.
Benchmark: Curated source set: NVIDIA Developer + MIT CSAIL + MIT OpenCourseWare + Open Robotics / Gazebo + MoveIt -- Credential note: this is a Zuyini mastery certificate built from the cited public courses. It is not issued, sponsored or endorsed by the source institutions, providers or companies.
Core Course Stack
1. NVIDIA Isaac Sim
2. NVIDIA Isaac Lab
3. Robotic Manipulation - 2026 Working Notes
4. Robotic Manipulation - Lecture Videos
5. Underactuated Robotics - Video Lectures
6. Gazebo Harmonic
7. MoveIt 2 Tutorials
8. Cognitive Robotics - Lecture Videos
Hands-On Mastery Capstone
Build a physical-AI prototype entirely in simulation or with optional hardware. Create the robot and environment, integrate perception and control, generate or use synthetic data, train or configure a policy/planner, evaluate safety and task success, and define a sim-to-real plan.
Proof Rubric
- simulation world
- robot/sensor model
- perception/planning/control stack
- synthetic-data or training pipeline
- eval suite
- safety constraints
- task-success metrics
- sim-to-real risk register