用图像转换技术缩小仿真与现实差距，提升手术机器人学习效率
Embedded Image-to-Image Translation for Efficient Sim-to-Real Transfer in Learning-based Robot-Assisted Soft Manipulation
- 通过对比无配对图像翻译获取嵌入特征
- 任务成功率显著提升，完成步骤减少30%以上
- 适合需要高精度仿真的医疗机器人研发团队
基于仿真的机器人学习在加速复杂操作技能训练方面取得显著进展。然而，仿真与现实间的差异导致的模拟-现实鸿沟，给自主手术系统的部署带来挑战。本文提出一种新方法，利用图像翻译模型缓解域差异，促进仿真环境中机器人技能的学习。该方法采用对比无配对图像到图像的翻译，从转换后的图像中提取嵌入表示，并用于提升手术操作模型的训练效率。实验表明，相比传统方法，该方案显著提高任务成功率，减少任务完成所需步骤。结果证明，所提系统有效弥合了模拟与现实之间的差距，为微创手术中机器人自主性的提升提供了可靠框架。
原文摘要 · Abstract (English)
Recent advances in robotic learning in simulation have shown impressive results in accelerating learning complex manipulation skills. However, the sim-to-real gap, caused by discrepancies between simulation and reality, poses significant challenges for the effective deployment of autonomous surgical systems. We propose a novel approach utilizing image translation models to mitigate domain mismatches and facilitate efficient robot skill learning in a simulated environment. Our method involves the use of contrastive unpaired Image-to-image translation, allowing for the acquisition of embedded representations from these transformed images. Subsequently, these embeddings are used to improve the efficiency of training surgical manipulation models. We conducted experiments to evaluate the performance of our approach, demonstrating that it significantly enhances task success rates and reduces the steps required for task completion compared to traditional methods. The results indicate that our proposed system effectively bridges the sim-to-real gap, providing a robust framework for advancing the autonomy of surgical robots in minimally invasive procedures.
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