用真实数据训练轮式机器人动力学模型，提升仿真精度。
D4W: Dependable Data-Driven Dynamics for Wheeled Robots
- 基于真实传感器数据学习机器人动力学，替代传统简化模型。
- 仿真精度优于传统方法，减少实际调试需求。
- 可对接现有仿真器与控制器，适合算法快速迭代开发。
轮式机器人因其在制造、物流和服务领域的广泛应用而备受关注。然而，由于难以构建高精度的动力学模型，其控制算法的开发与测试仍面临挑战，需大量物理实验。为此，本文提出D4W（Dependable Data-Driven Dynamics for Wheeled Robots），一种融合数据驱动方法的仿真框架，以加速轮式机器人算法的研发与评估。核心贡献是利用真实世界传感器数据学习精确的动力学模型，该模型能捕捉复杂机器人行为及其与环境的交互，突破了解析方法仅适用于简化场景的局限。实验表明，D4W在仿真精度上优于传统方法，支持轮式机器人算法的快速迭代，且几乎无需现实中的调参。我们通过与现有仿真器和控制器的集成，进一步验证了该框架的可用性与实用性。
原文摘要 · Abstract (English)
Wheeled robots have gained significant attention due to their wide range of applications in manufacturing, logistics, and service industries. However, due to the difficulty of building a highly accurate dynamics model for wheeled robots, developing and testing control algorithms for them remains challenging and time-consuming, requiring extensive physical experimentation. To address this problem, we propose D4W, i.e., Dependable Data-Driven Dynamics for Wheeled Robots, a simulation framework incorporating data-driven methods to accelerate the development and evaluation of algorithms for wheeled robots. The key contribution of D4W is a solution that utilizes real-world sensor data to learn accurate models of robot dynamics. The learned dynamics can capture complex robot behaviors and interactions with the environment throughout simulations, surpassing the limitations of analytical methods, which only work in simplified scenarios. Experimental results show that D4W achieves the best simulation accuracy compared to traditional approaches, allowing for rapid iteration of wheel robot algorithms with less or no need for fine-tuning in reality. We further verify the usability and practicality of the proposed framework through integration with existing simulators and controllers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。