用单块GPU实现高速手术机器人强化学习仿真，训练速度提升10倍。
FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning
- 全在单个GPU上运行物理模拟与强化学习，避免数据传输瓶颈。
- 复杂组织操作任务训练时间缩短至几分钟，相比传统系统快10倍。
- 适合研究手术机器人自动化与强化学习算法的开发者使用。
机器人手术是快速发展的领域，自动化手术任务可显著受益于强化学习（RL）技术。然而，RL需要大量任务重复，真实手术系统难以安全高效地执行。因此亟需既逼真又计算高效、可扩展的手术仿真环境。本文提出FF-SRL（Fast and Flexible Surgical Reinforcement Learning），一个高性能的机器人手术学习环境。在FF-SRL中，物理模拟与强化学习策略训练均部署于单一GPU上，避免了传统架构中CPU与GPU间的数据传输瓶颈，显著提升训练效率。实验结果表明，相较于常见的CPU/GPU混合仿真器，复杂组织操作任务的训练时间缩短一个数量级，仅需数分钟。该加速效果有助于推动强化学习方法在手术机器人中的试验与应用。代码已开源，供社区使用。
原文摘要 · Abstract (English)
Robotic surgery is a rapidly developing field that can greatly benefit from the automation of surgical tasks. However, training techniques such as Reinforcement Learning (RL) require a high number of task repetitions, which are generally unsafe and impractical to perform on real surgical systems. This stresses the need for simulated surgical environments, which are not only realistic, but also computationally efficient and scalable. We introduce FF-SRL (Fast and Flexible Surgical Reinforcement Learning), a high-performance learning environment for robotic surgery. In FF-SRL both physics simulation and RL policy training reside entirely on a single GPU. This avoids typical bottlenecks associated with data transfer between the CPU and GPU, leading to accelerated learning rates. Our results show that FF-SRL reduces the training time of a complex tissue manipulation task by an order of magnitude, down to a couple of minutes, compared to a common CPU/GPU simulator. Such speed-up may facilitate the experimentation with RL techniques and contribute to the development of new generation of surgical systems. To this end, we make our code publicly available to the community.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。