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This chapter explores the role of simulation in robotics as a costeffective and scalable method for training robots, addressing challenges like the Sim2Real gap through domain adaptation and domain randomization. It discusses popular simulators like PyBullet, MuJoCo, and Gazebo, as well as methods like RL-CycleGAN for translating simulated data into real-world contexts. The chapter also highlights how learning from both simulated and real-world data enhances task adaptability, leveraging LLMs, foundation models, and world modeling techniques for improved robotic skill acquisition.

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Simulation

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  • 发表日期：

    2025年

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  • Alishba Imran,
  • Keerthana Gopalakrishnan

摘要

This chapter explores the role of simulation in robotics as a costeffective and scalable method for training robots, addressing challenges like the Sim2Real gap through domain adaptation and domain randomization. It discusses popular simulators like PyBullet, MuJoCo, and Gazebo, as well as methods like RL-CycleGAN for translating simulated data into real-world contexts. The chapter also highlights how learning from both simulated and real-world data enhances task adaptability, leveraging LLMs, foundation models, and world modeling techniques for improved robotic skill acquisition.