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Simulation environments are essential for training and evaluating embodied AI agents safely and at scale before deploying to real robots.

Habitat

Meta’s platform for embodied AI research with photorealistic 3D environments.

PyRobot

Meta’s robotics framework for running robot learning algorithms.

Isaac Sim

NVIDIA’s robotics simulation platform with physics and sensor simulation.

MuJoCo

Physics engine for research in robotics, biomechanics, and machine learning.

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Key Concepts

  • Physics Simulation: Realistic dynamics for contact, friction, and manipulation
  • Sensor Simulation: Cameras, LiDAR, depth sensors, and proprioception
  • Domain Randomization: Varying simulation parameters for robust transfer
  • Parallel Environments: Scaling training with multiple simultaneous instances
Key references: (Bousmalis et al., 2017; Arulkumaran et al., 2017; Sadeghi & Levine, 2016)

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References

  • Arulkumaran, K., Deisenroth, M., Brundage, M., Bharath, A. (2017). A Brief Survey of Deep Reinforcement Learning.
  • Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., et al. (2017). Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping.
  • Sadeghi, F., Levine, S. (2016). CAD2RL: Real Single-Image Flight without a Single Real Image.
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