Efficient physics simulation has significantly accelerated research progress in robotics applications such as grasping and assembly. The advent of GPU-accelerated simulation frameworks like Isaac Sim has particularly empowered learning-based methods, enabling them to tackle increasingly complex tasks. The PAL Robotics TIAGo++ Omni is a versatile mobile manipulator equipped with a mecanum-wheeled base, allowing omnidirectional movement and a wide range of task capabilities. However, until now, no model of the robot has been available in Isaac Sim. In this paper, we introduce such a model, calibrated to approximate the behavior of the real robot, with a focus on its omnidirectional drive dynamics. We present two control models for the omnidirectional drive: a physically accurate model that replicates real-world wheel dynamics and a lightweight velocity-based model optimized for learning-based applications. With these models, we introduce a learning-based calibration approach to approximate the real robot's S-shaped velocity profile using minimal trajectory data recordings. This simulation should allow researchers to experiment with the robot and perform efficient learning-based control in diverse environments. We provide the integration publicly at https://github.com/AIS-Bonn/tiago_isaac.

翻译：高效的物理仿真显著加速了机器人抓取与装配等应用领域的研究进展。以Isaci Sim为代表的GPU加速仿真框架的出现，特别强化了基于学习的方法，使其能够处理日益复杂的任务。PAL Robotics公司开发的TIAGo++ Omni是一款多功能移动机械臂，配备麦克纳姆轮式底盘，可实现全向运动并具备广泛的任务执行能力。然而，迄今为止Isaac Sim中尚未提供该机器人的仿真模型。本文提出了一种经过标定的机器人模型，该模型能够近似模拟真实机器人的行为特性，并重点关注其全向驱动动力学。我们提出了两种全向驱动控制模型：一种能精确复现真实轮系动力学的物理精确模型，以及一种专为基于学习的应用优化的轻量化速度控制模型。基于这些模型，我们引入了一种基于学习的标定方法，仅需少量轨迹数据记录即可近似重构真实机器人的S形速度剖面。该仿真系统将使研究人员能够在多样化环境中对机器人进行实验，并实现高效的基于学习的控制。相关集成代码已在https://github.com/AIS-Bonn/tiago_isaac公开提供。