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AI / Robotics

Sim2Real Transfer

Bridging the gap between simulated and real-world environments for robotics applications using domain adaptation techniques.

Problem

Models trained in simulation often fail in real-world deployment due to the sim-to-real gap.

Solution

Implemented domain adaptation techniques to transfer learned policies from simulated environments to real-world scenarios.

Architecture

1

Simulation

Training in simulated environment

2

Domain Adaptation

Bridging sim-to-real gap

3

Real-World Transfer

Deploying adapted policy

Technologies

PythonPyTorchReinforcement LearningSimulationRobotics

Lessons Learned

  • →

    Domain randomization helps bridge the sim-to-real gap

  • →

    Simulation fidelity directly correlates with transfer success