基于GPU的手术机器人仿真引擎，支持多物理场并行模拟与学习。
CRESSim-Neo: A Batched GPU Simulation Engine for Surgical Robotics and Robot Learning
- 采用位置约束法模拟刚体、软组织、流体与绳索，批量渲染并集成手术感知
- 在RTX 4090上实现每秒203万次环境步长（8192个并行任务）
- 适合手术机器人学习、合成数据生成与零拷贝深度学习应用
我们提出CRESSim-Neo，一种用于手术机器人与机器人学习的批处理式GPU仿真引擎。该引擎结合刚体、可变形组织、流体与细丝的位置约束模拟，支持批量渲染、手术专用传感及驻留于GPU的数据流水线。可应用于组织操作、液体抽吸、缝合、缆绳驱动机器人及超声图像生成。通过直接访问物理与渲染缓冲区，支持驻留于GPU的机器人学习，并利用DLPack实现零拷贝PyTorch集成。我们在刚体、可变形体和流体模拟任务中验证了该引擎，涵盖视觉引导与手术机器人学习场景。在NVIDIA RTX 4090上，对8192个并行CartPole环境最高达每秒203万次环境步长，可扩展至包含组织变形、流体交互与超声感知的批量手术场景。整体而言，CRESSim-Neo为手术仿真、合成数据生成与手术机器人学习提供统一且可扩展的平台。
原文摘要 · Abstract (English)
We introduce CRESSim-Neo, a batched GPU simulation engine for surgical robotics and robot learning. CRESSim-Neo combines position-based simulation of rigid bodies, deformable tissues, fluids, and strands with batched rendering, surgery-specific sensing, and a GPU-resident data pipeline. The engine supports applications including tissue manipulation, fluid suction, suturing, cable-driven robots, and ultrasound image synthesis. Direct access to physics and rendering buffers enables GPU-resident robot learning and zero-copy PyTorch integration using DLPack. We demonstrate CRESSim-Neo across rigid-body, deformable-body, and fluid simulation tasks, including vision-based and surgical robot-learning scenarios. On an NVIDIA RTX 4090, the engine achieves up to 2.03 million environment steps per second for 8192 parallel CartPole environments, and scales to batched surgical scenarios involving tissue deformation, fluid interaction, and ultrasound sensing. Overall, CRESSim-Neo provides a unified and scalable platform for surgical simulation, synthetic data generation, and surgical robot learning.
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