Integration of the TIAGo Robot into Isaac Sim with Mecanum Drive Modeling and Learned S-Curve Velocity Profiles

2025-10-11

AI Summary

Existing Isaac Sim implementations lack a high-fidelity simulation model for the PAL Robotics TIAGo++ Omni—a holonomic mobile manipulator equipped with Mecanum wheels—hindering its use in holonomic dynamics modeling and learning-based control research. To address this, we propose a dual-mode drive modeling approach: (i) a high-fidelity physics-based model accurately capturing Mecanum wheel–ground contact dynamics, and (ii) a lightweight velocity-level model enabling real-time control. Innovatively, we introduce S-curve velocity profile learning from minimal trajectory data, coupled with a minimal-data-driven parameter calibration mechanism, substantially reducing modeling dependency. Leveraging Isaac Sim’s GPU-accelerated framework, our implementation achieves efficient simulation. The resulting open-source model significantly improves training efficiency of learning-based algorithms for mobile manipulation tasks and enhances sim-to-real transfer performance.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Manipulation

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Modeling TIAGo robot's omnidirectional drive dynamics in Isaac Sim
• Developing control models for accurate physics and learning-based applications
• Calibrating S-curve velocity profiles using minimal trajectory data

Innovation

• Integrated TIAGo robot model into Isaac Sim
• Developed two omnidirectional drive control models
• Used learning-based calibration for velocity profiles