发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文推出CRESSim-Neo，一款面向外科机器人与机器人学习的批量GPU仿真引擎，支持多类外科相关仿真任务，在RTX 4090上实现高并行环境步性能，为相关研究提供统一可扩展平台。
AI 中文摘要
我们推出CRESSim-Neo，这是一款面向外科机器人与机器人学习的批量GPU仿真引擎。CRESSim-Neo将基于位置的刚体、可变形组织、流体及丝状物仿真，与批量渲染、外科专用传感以及驻留于GPU的数据流相结合。该引擎支持组织操作、流体抽吸、缝合、缆索驱动机器人、超声图像合成等应用。对物理与渲染缓冲区的直接访问，实现了驻留于GPU的机器人学习，以及使用DLPack的零拷贝PyTorch集成。我们在刚体、可变形体及流体仿真任务（包括基于视觉的外科机器人学习场景）中验证了CRESSim-Neo。在NVIDIA RTX 4090上，该引擎针对8192个并行CartPole环境，实现了每秒高达203万环境步的性能，并可扩展至包含组织变形、流体交互及超声传感的批量外科场景。总体而言，CRESSim-Neo为外科仿真、合成数据生成及外科机器人学习提供了统一且可扩展的平台。
英文摘要
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.
Comments8 pages, 11 figures
