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Synthetic Simulation Pipelines for Embodied AI

Design high-fidelity simulation stacks that combine physics accuracy, procedural scene generation, and language-driven scenario scripting to accelerate robotics training.

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12. Case Study: Robotics Manipulation Platform

Imagine building a pipeline for robotic manipulation.

  • Scene Library: Warehouse shelves, bins, and conveyor belts with randomized clutter.
  • Physics: High-fidelity gripper dynamics, friction modeling, and collision detection.
  • Sensor Suite: RGB-D cameras, tactile arrays, and proprioception.
  • Curriculum: Start with single-object pick-and-place, progress to multi-object sorting with dynamic obstacles.
  • Transfer: Deploy models to real robots in a controlled lab, collect failure logs, and feed them back into scenario generators.
  • Governance: Review each new asset for safety (no offensive markings), maintain licensing records, and require dual approval for curriculum changes affecting safety-critical motions.
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