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Physics-Based Simulation for Robotics: Simulating real-world environments for training and validation

Author(s) Ruchik Kashyapkumar Thaker
Country United States
Abstract Physics-based simulation plays a vital role in the development, testing, and verification of robotic control algorithms and designs by offering a fast, safe, and cost-effective environment for generating labeled training data. This paper provides an overview of how simulation models robot dynamics, sensing, and interactions with both environments and humans. While simulation can serve as a powerful tool for intelligent robot development, its widespread adoption is hindered by challenges such as fragmented tools and varying performance across platforms like MuJoCo, Bullet, Havok, ODE, and PhysX. I explore these limitations and compare physics-based simulators with alternatives like Artificial Neural Networks (ANNs) in Evolutionary Robotics (ER). Finally, the paper presents recommendations for overcoming barriers and improving simulation tools to enhance intelligent robot design.
Keywords Physics-based simulation, Robotics, Virtual prototyping, Robot dynamics, Simulation tools, Machine learning in robotics, Intelligent robot design
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 3, Issue 6, June 2022
Published On 2022-06-07
Cite This Physics-Based Simulation for Robotics: Simulating real-world environments for training and validation - Ruchik Kashyapkumar Thaker - IJLRP Volume 3, Issue 6, June 2022. DOI 10.5281/zenodo.14001868
DOI https://doi.org/10.5281/zenodo.14001868
Short DOI https://doi.org/g8n868
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