High-Fidelity Robotics Simulation & Autonomy Testing Platform
Project MotionForgeSim
Client:
NDA-protected enterprise
Duration:
10 months
Team:
2 simulation engineers, 2 software engineers, 1 ML engineer
The Problem
The robotics team lacked a scalable and realistic virtual testing environment, leading to slow iteration cycles and high physical testing costs.
What We Did
Built a physics-accurate simulation engine, integrated sensor emulation, added reinforcement-learning training hooks, and deployed a scenario generator for automated testing.
Outcome
Reduced physical testing by 60%, accelerated autonomy development cycles by 3×, and improved simulation-to-reality transfer reliability.
Operational Impact
Automated environment scenarios. Reduced risk during prototyping. Faster validation of autonomous behaviors.
Key Challenges
1
Physics Accuracy
Ensuring stable simulations across diverse robot geometries and loads.
2
Sensor Fidelity
Matching simulated sensor output closely with real hardware performance.
What Made This Work
High-Fidelity Physics Engine
Calibrated physics simulation matching real-world hardware behavior.
Sensor-Accurate Training
Realistic sensor emulation for vision, LiDAR, and IMU systems.
Scalable Scenario Generation
Automated testing across thousands of edge cases and failure modes.