将TIAGo机器人导入Isaac Sim，支持全向移动与学习控制。
Integration of the TIAGo Robot into Isaac Sim with Mecanum Drive Modeling and Learned S-Curve Velocity Profiles
- 构建了基于物理的全向轮模型与轻量速度模型。
- 仅用少量轨迹数据学习出S型速度曲线，逼近真实表现。
- 适合做机器人强化学习与仿真验证的研究者使用。
高效物理仿真显著推动了机器人抓取与装配等应用的研究进展。GPU加速的仿真框架如Isaac Sim，尤其赋能基于学习的方法，使其能应对更复杂的任务。PAL Robotics TIAGo++ Omni是一款配备全向轮底盘的多功能移动操作机器人，可实现全方位运动和多样任务。然而，此前该机器人尚未在Isaac Sim中可用。本文提出其仿真模型，经校准以近似真实机器人行为，重点在于全向驱动动力学。我们设计两种驱动控制模型：一种物理精确模型，还原真实轮子动态；另一种面向学习应用的轻量级速度模型。结合二者，提出基于学习的校准方法，仅需少量轨迹数据即可逼近真实机器人的S型速度特性。该仿真环境使研究者可在多样化场景中实验与进行高效学习控制。代码已公开于https://github.com/AIS-Bonn/tiago_isaac。
原文摘要 · Abstract (English)
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.
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