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The Radiance of Neural Fields: Democratizing Photorealistic and Dynamic Robotic Simulation

Georgina Nuthall, Richard Bowden, Oscar Mendez

TL;DR

This work has developed a simulator that incorporates three essential elements: photorealistic neural rendering of environments, neurally animated human entities with behaviour management, and an ego-centric robotic agent providing multi-sensor output, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering.

Abstract

As robots increasingly coexist with humans, they must navigate complex, dynamic environments rich in visual information and implicit social dynamics, like when to yield or move through crowds. Addressing these challenges requires significant advances in vision-based sensing and a deeper understanding of socio-dynamic factors, particularly in tasks like navigation. To facilitate this, robotics researchers need advanced simulation platforms offering dynamic, photorealistic environments with realistic actors. Unfortunately, most existing simulators fall short, prioritizing geometric accuracy over visual fidelity, and employing unrealistic agents with fixed trajectories and low-quality visuals. To overcome these limitations, we developed a simulator that incorporates three essential elements: (1) photorealistic neural rendering of environments, (2) neurally animated human entities with behavior management, and (3) an ego-centric robotic agent providing multi-sensor output. By utilizing advanced neural rendering techniques in a dual-NeRF simulator, our system produces high-fidelity, photorealistic renderings of both environments and human entities. Additionally, it integrates a state-of-the-art Social Force Model to model dynamic human-human and human-robot interactions, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering.

The Radiance of Neural Fields: Democratizing Photorealistic and Dynamic Robotic Simulation

TL;DR

This work has developed a simulator that incorporates three essential elements: photorealistic neural rendering of environments, neurally animated human entities with behaviour management, and an ego-centric robotic agent providing multi-sensor output, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering.

Abstract

As robots increasingly coexist with humans, they must navigate complex, dynamic environments rich in visual information and implicit social dynamics, like when to yield or move through crowds. Addressing these challenges requires significant advances in vision-based sensing and a deeper understanding of socio-dynamic factors, particularly in tasks like navigation. To facilitate this, robotics researchers need advanced simulation platforms offering dynamic, photorealistic environments with realistic actors. Unfortunately, most existing simulators fall short, prioritizing geometric accuracy over visual fidelity, and employing unrealistic agents with fixed trajectories and low-quality visuals. To overcome these limitations, we developed a simulator that incorporates three essential elements: (1) photorealistic neural rendering of environments, (2) neurally animated human entities with behavior management, and (3) an ego-centric robotic agent providing multi-sensor output. By utilizing advanced neural rendering techniques in a dual-NeRF simulator, our system produces high-fidelity, photorealistic renderings of both environments and human entities. Additionally, it integrates a state-of-the-art Social Force Model to model dynamic human-human and human-robot interactions, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering.

Paper Structure

This paper contains 22 sections, 5 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Robotic Simulators
  4. Neural Rendering Approaches
  5. Robot Navigation in Crowds
  6. METHODOLOGY
  7. Neural Rendering of Local Environments
  8. Neural Radiance Fields
  9. Our nerf
  10. Animated Human Entities with Behavior Management
  11. Ego-centric Robot Agent with Multi-Sensor Integration
  12. Appearance Sensors
  13. Spatial Sensors
  14. System Architecture and Components
  15. EXPERIMENTS AND RESULTS
  16. ...and 7 more sections

Figures (5)

  • Figure 2: System Overview – The figure illustrates the components of our simulation pipeline. (1) Shows the training of a local environment based on user-provided video footage. (2) Depicts the integration of human entity representations, trained using motion capture data and the social force model, for dynamic human behavior simulation. (3) (i) Details the integration of a predefined robot, exemplified here by Spot’s URDF, into the simulator. All elements are aligned within the same coordinate frame to enable accurate multi-sensor output rendering (3) (ii). The bottom left shows simulated LiDAR output, while the bottom right presents the simulated RGB stereo output.
  • Figure 3: Sample Gait Cycle using Motion Capture
  • Figure 4: Depth Image Comparison: (Left) Real Back Depth Image and (Right) Simulated Back Depth Image for Comparison
  • Figure 5: (Left) Front-Left and (Right) Front-Right Simulated Camera Renders
  • Figure 6: Object Detection Evaluation – Comparison of object detection performance across simulated environments and 3D mesh reconstructions