Demo: Closed-Loop Sionna-Isaac Sim Co-Simulation Framework for Wireless-Aware Robot Navigation over ROS 2

Yi Shen Lim; Seungeun Oh; Jihong Park

cs.NIOct 6, 2026

Demo: Closed-Loop Sionna-Isaac Sim Co-Simulation Framework for Wireless-Aware Robot Navigation over ROS 2

Authors: Yi Shen Lim, Seungeun Oh, Jihong Park

Organizations: Singapore University of Technology and Design Singapore

Abstract

A robot that offloads its control loop to the network carries the receiver with it, so link quality is decided by where it goes. Simulating this requires both a physics engine and a site-specific propagation model at once; to our knowledge no simulator natively unifies both, with existing couplings of the two limited to offline analyses. We demonstrate a real-time co-simulation framework coupling NVIDIA Isaac Sim and NVIDIA Sionna over ROS 2 that closes the perception-action-communication (PAC) loop between them. Sionna ray-traces the base-station-to-robot channel over the exact geometry Isaac Sim simulates on, rather than modeling it stochastically, and feeds channel states back into the control loop in real time. Ray-traced on GPU, the coverage map is refreshed in ~16ms (~60 Hz), fast enough for real-time control. To showcase the framework's utility, we implement a wireless-aware navigation application in an OpenStreetMap(OSM)-derived SUTD campus twin with two Nova Carter robots: the closed-loop planner eliminates communication outage at only +7.4% traversal time over the shortest-path baseline (which spends 7.9 s of its 81.2 s run disconnected).

Figures & tables

Figure 1. Co-simulation view: Isaac Sim simulates robots, Sionna ray-traces channels, ROS 2 relays states, RViz2 (ROS 2’s visualization tool) overlays RSS heatmap and planned routes; attendees drive all views live in Scenario 1.

Figure 2. Routes (a) and RSS along each traversal (b) for $\alpha{=}0$ (red), $10^{-2}$ (blue), $10^{-1}$ (green), $1$ (black) over the ray-traced coverage map; Tx positions marked, sub-threshold region shaded; outage falls from $7.9$ s (red) to $0$ s (black).

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Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds

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Department of Electrical and Computer Engineering, University of Louisville · Faculty of Electrical and Computer Engineering, University of Louisville, Louisville, KY, 40208, USA

Sim-To-Real Reinforcement Learning Locomotion Sim-To-Real Gap Scalable Robot Learning Whole-Body Control

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Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body control. Reinforcement learning (RL), the primary learning-based approach for locomotion, often utilizes a high-performance simulation tool, providing a controlled and efficient training and development environment. However, policies that perform well in simulation frequently encounter unexpected challenges when deployed on a physical system, known as the sim-to-real gap. This work presents a robust RL locomotion framework capable of whole-body control. The proposed RL framework utilizes Nvidia's new set of simulation tools, Isaac Sim, and its companion RL framework, Isaac Lab, for training, achieving a zero-shot sim-to-real policy. The performance of our policy is validated on physical hardware using the Unitree Go1, with experimental results showing similar velocity tracking performance to the quadruped's integrated controller, with a greater ability to recover from large disturbances, and achieve linear velocities of 2.0 m/s and angular velocities of 1.8 rad/s.

Abstract

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