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Demonstrating Arena 5.0: A Photorealistic ROS2 Simulation Framework for Developing and Benchmarking Social Navigation
Volodymyr Shcherbyna, Linh Kästner, Duc Anh Do, Hoang Tung, Huu Giang Nguyen, Maximilian Ho-Kyoung Schreff, Tim Seeger, Eva Wiese, Ahmed Martban, Huajian Zeng, An Tran, Nguyen Quoc Hung, Jonas Kreutz, Vu Thanh Lam, Ton Manh Kien, Harold Soh
TL;DR
Social-navigation research faces reproducibility and practical-applicability challenges, while existing platforms can be limited in realism, functionality, and accessibility. Arena 5.0 integrates Isaac Gym with Arena's generation and benchmarking modules; user feedback primarily highlighted positive advancements and praised the integration and simplified wrappers.
Problem
Social-navigation approaches can be difficult to reproduce, generalize, and assess for practical use, motivating realistic platforms for testing before deployment.
Source: §I · p. 1Method
Arena 5.0 integrates Isaac Gym with Arena's social-environment, benchmarking, and scenario-generation capabilities.
Source: §I · p. 2, A. System Design of Arena 5.0 · p. 3Results
User feedback primarily highlighted positive advancements in Arena 5.0 and praised the Isaac Gym integration and simplified wrappers.
Source: 1) User Study · p. 7Takeaways & Limitations
Arena 5.0 is open source and provides APIs for integrating planners and extending platform capabilities.
Source: §VI · p. 8Photorealistic simulation requires more computation and has higher latency than lighter-weight simulators.
Source: §V · p. 7
Abstract
from arXiv · showhide
Building upon the foundations laid by our previous work, this paper introduces Arena 5.0, the fifth iteration of our framework for robotics social navigation development and benchmarking. Arena 5.0 provides three main contributions: 1) The complete integration of NVIDIA Isaac Gym, enabling photorealistic simulations and more efficient training. It seamlessly incorporates Isaac Gym into the Arena platform, allowing the use of existing modules such as randomized environment generation, evaluation tools, ROS2 support, and the integration of planners, robot models, and APIs within Isaac Gym. 2) A comprehensive benchmark of state-of-the-art social navigation strategies, evaluated on a diverse set of generated and customized worlds and scenarios of varying difficulty levels. These benchmarks provide a detailed assessment of navigation planners using a wide range of social navigation metrics. 3) Extensive scenario generation and task planning modules for improved and customizable generation of social navigation scenarios, such as emergency and rescue situations. The platform's performance was evaluated by generating the aforementioned benchmark and through a comprehensive user study, demonstrating significant improvements in usability and efficiency compared to previous versions. Arena 5.0 is open source and available at https://github.com/Arena-Rosnav.
I. INTRODUCTION
Social navigation research needs realistic, comprehensive evaluation to address reproducibility, generalizability, and practical-applicability challenges. Arena 5.0 responds with Isaac Gym integration, benchmarking, and customizable scenario generation.
I. INTRODUCTION
- Existing platforms face reproducibility and generalizability problems, while many offer limited functionality, realism, or practical applicability.The paper also identifies legacy software and usability barriers in sophisticated simulators as obstacles to wider use.Source: §I · p. 1
- Arena 5.0 integrates Isaac Gym into the Arena framework to support photorealistic simulation and robot development and testing.The integration includes comprehensive ROS integration for interoperability with ROS-based systems.Source: §I · p. 2
- Arena 5.0 establishes benchmarks for social navigation algorithms using metrics for accuracy, efficiency, and real-time performance.Source: §I · p. 2
- Its customizable scenario-generation pipelines create diverse, complex environments for specific research needs.The stated aim is to accelerate testing and refinement of navigation algorithms.Source: §I · p. 2
II. RELATED WORKS
Prior social-navigation benchmarks vary in realism, scenario and robot diversity, sensor coverage, and software support. Arena 5.0 addresses these gaps with multiple simulation environments, sensors, robots, planners, and generation modules.
II. RELATED WORKS
- Existing benchmarks often trade off dynamic environments, realistic complexity, scenario diversity, robot variety, sensor modalities, and current software frameworks.Some focus on static 2D settings, while others use dynamic settings but offer simplistic proof-of-concept approaches.Source: §II · p. 2
- Arena's system design separates core world and task interfaces from peripheral generation and evaluation modules, with lifecycle management centralized in arena_bringup.The Python-only modules provide an extensible API for interactions with Arena and ROS2.Source: Figure 3 · p. 3
- Isaac Gym offers diverse, complex dynamic environments, but its usability challenges and limited openness restrict access beyond experts.Arena 5.0 proposes a broader suite of simulation environments, sensor modalities, robots, planners, and world and scenario generation modules.Source: §II · p. 3
III. OVERVIEW OF ARENA 5.0
Arena 5.0 is a modular stack for developing and benchmarking social-navigation methods. Its contributions include Isaac Gym integration, multi-modal benchmarking, and generative world and scenario creation.
III. OVERVIEW OF ARENA 5.0
- Arena 5.0 combines Isaac Gym integration, multi-modal benchmarking across robotic platforms, and generative-AI and LLM modules for creating social scenarios and environments.Source: §III · p. 3
A. System Design of Arena 5.0
- The platform generates social environments in photorealistic simulators such as Unity and Isaac Gym, with options to spawn humans and specify their behaviors.These options support simulation of specific situations, including emergencies.Source: A. System Design of Arena 5.0 · p. 3
- Isaac Gym integration adds support for creating scenarios, benchmarking, and running planners in Isaac Sim within the Arena platform.Source: A. System Design of Arena 5.0 · p. 3
B. Isaac Gym Integration
Arena wraps Isaac Gym to make it more accessible within its framework and support photorealistic social-navigation scenarios. The platform also models pedestrian interactions and behavior through interchangeable simulation components.
B. Isaac Gym Integration
- An Isaac Gym wrapper lowers entry barriers associated with limited documentation, tutorials, and support, while enabling photorealistic social-navigation scenarios.The integration supports testing robots in complex interactive environments intended to closely mimic real-world settings.Source: B. Isaac Gym Integration · p. 3
- arena_gen transforms a text prompt or floorplan into a 3DSG, floorplan, populated world, and editable scenario for training and evaluation.Worlds include map definitions and structural and semantic annotations for simulator loading and task construction.Source: Figure 4 · p. 4
1) Pedestrian Simulation:
- Pedestrian simulation combines human-interaction models, behavior-dependent animations, and an EntityManager interface that supports different pedestrian simulators and social-force models.Arena selects pedestrian models for scenario requirements and interpolates animations based on Social Force Model evaluations and behavior state.Source: 1) Pedestrian Simulation · p. 4
C. World and Scenario Generation
Arena 5.0 generates randomized worlds from floorplans or natural-language prompts, then populates them with room-aware object placement. It also supports configurable scenarios, including routine and unexpected situations.
C. World and Scenario Generation
- Worlds can be generated gradually across Gazebo and Isaac Sim, with static and dynamic obstacles in settings such as homes, hospitals, offices, and warehouses.Users can start from a 2D floorplan or a natural-language environment prompt.Source: C. World and Scenario Generation · p. 4
- The 3dsg_from_text pipeline separates room-and-doorway generation from room-object inference using two language models.The room and asset type lists are supplied as system prompts and can be changed without retraining.Source: Figure 5 · p. 4
- MiDiffusion places objects room by room in increasingly realistic positions, preserving pathways that support human-object interactions.The process combines the 3D Scene Graph with the floorplan and takes room type into account.Source: C. World and Scenario Generation · p. 5
- Users can configure routine, emergency, and unexpected scenarios, including human behaviors such as running toward, playing with, or obstructing robots.These scenarios can be selected or parameterized in the GUI for testing under varied conditions.Source: C. World and Scenario Generation · p. 5
D. Benchmarking and Dataset Acquisition Modules
Arena 5.0 integrates multiple sensor types and an evaluation pipeline for collecting, processing, and plotting data. The paper also demonstrates the web application and reports a participant study of platform modules.
D. Benchmarking and Dataset Acquisition Modules
- The platform supports 2D and 3D lidar, RGB and RGBD cameras, IMU, sonar, and foot-contact sensors across simulators.Its evaluation pipeline plots results in a customizable iPython notebook and extends the metrics suite with social metrics.Source: D. Benchmarking and Dataset… · p. 5
IV. DEMONSTRATIONS AND EXEMPLARY USAGE
- The demonstration section presents the web application’s customized world-and-scenario workflow and a study in which participants installed and tested platform modules.Participants were asked to install and test specific modules.Source: §IV · p. 5
A. Creating Worlds and Scenarios Using the GUI
The GUI supports a configurable workflow from world creation through scenario setup and benchmark task selection. Arena 5.0 also provides APIs and generated worlds for unified planner evaluation.
A. Creating Worlds and Scenarios Using the GUI
- Users can upload a floorplan, enter a prompt, or choose a prebuilt world, then generate a 3D scene graph, floorplan, and customized pedestrian or obstacle scenario.Pedestrian paths and other behavior parameters can be set individually, or users can select preset situations such as hospital routines or emergencies.Source: A. Creating Worlds and Scenarios Using… · pp. 5–6
- Users set a benchmark count for each task mode to specify how many robot runs each benchmark includes.Source: A. Creating Worlds and Scenarios Using… · p. 6
B. Platform for Competition and Benchmarking
- Arena-Gen creates diverse worlds and scenarios for consistent testing, while APIs support integration of methods and new planning algorithms.Users can test approaches in predefined or custom scenarios.Source: B. Platform for Competition and… · p. 6
- Planners were run 15 times on each of four difficulty levels for hospital, office, and warehouse worlds, with metrics averaged across runs.The benchmark covers the planners listed in Table I and presents their metrics in Figure 10.Source: B. Platform for Competition and… · p. 6
1) User Study:
A 20-participant study collected feedback after users installed and tested Arena 5.0 modules. Participants generally favored the Isaac Gym wrappers, web interface, and Arena-Gen, while also noting stability and computational concerns.
1) User Study:
- The study involved 20 participants from universities in Germany, the US, Singapore, Japan, and Korea, with varied robotics expertise and prior Arena experience.Participants installed and interacted with the platform by launching task modes, training runs, and planner tests.Source: 1) User Study · p. 6
- Participants praised the Isaac Gym integration and simplified wrappers, and found the web interface more intuitive than the previous JSON-based workflow.Users reported that the wrappers helped them quickly generate scenarios and benchmark approaches in Isaac Sim.Source: 1) User Study · p. 7
- Two participants reported occasional stability concerns, including longer waits for world-generation prompts; a few cases produced unrealistic 3D worlds from incorrect floorplans.Source: 1) User Study · p. 7
- Users noted increased computational demand from Isaac Sim, while Arena’s modular architecture retains compatibility with multiple simulators.Source: 1) User Study · p. 7
V. LIMITATIONS
Arena 5.0’s photorealistic simulation brings higher computational overhead and latency, alongside limits in animation assets and world types. The conclusion describes an expanded, open-source platform and outlines further planned extensions.
V. LIMITATIONS
- Photorealistic simulation increases computational overhead and latency, though performance scales more favorably with environment complexity and agent count.The abstraction layer lets users select a simulator suited to their hardware and application needs.Source: §V · p. 7
- Human-motion visualization depends on existing animation assets, and creating new animations requires specialized 3D modeling expertise.Motion-capture transfer methods are planned for future integration.Source: §V · p. 7
- World generation is currently limited to office, hospital, and warehouse environments, with broader coverage identified as future work.The paper names globally sourced datasets as a priority for expanding that coverage.Source: §V · p. 7
VI. CONCLUSION
- Arena 5.0 combines Isaac Gym, user interfaces, scenario generation, benchmarking tools, APIs, and Nav2 within an open-source platform.Future plans include outdoor worlds, weather conditions, multi-agent reinforcement learning, and Arena-Web.Source: §VI · p. 8
VII. APPENDIX
The appendix provides an overview of robot and planner suites, evaluation metrics, world-generation materials, task modes, and an example benchmark definition.
VII. APPENDIX
- The robot-and-planner overview describes planner options and robot compatibility, while the metrics table lists evaluation measures.The caption notes that costmap planners integrate with Nav2 and accept any number and types of sensors.Source: Table I · p. 11, Table II · p. 11
- The appendix includes tables covering 3dsg_from_text training parameters and a dataset example.Source: Table III · p. 11, Table IV · p. 12
- A separate table provides an overview of task modes.Source: Table V · p. 12
- The example hospital care-robot benchmark evaluates a scenario five times, then completes 25 random tasks.Source: §VII · p. 12