Simulation | Liu Yue — Embodied AI Researcher

Endovascular Surgical Robot Platform (stEVE)

Sun, 01 Mar 2026 00:00:00 +0000

miracle-techlink / GitHub stEVE 平台代码仓库 GitHub Blog: VLA 如何落地医疗机器人 miracle-techlink.github.io · 综述文章 Link

A complete research platform for AI-assisted endovascular intervention, spanning preoperative 3D planning through intraoperative RL-guided navigation.

Pipeline:

CTA → STL — TotalSegmentator vessel segmentation, VMTK centerline extraction
SOFA FEM Simulation — Cosserat beam guidewire model, realistic vascular deformation
RL Navigation — SAC/PPO agents with geodesic distance reward + safety constraints
Biplane DSA — DLT triangulation + Beer-Lambert X-ray projection for 2D-3D registration
Intraoperative Correction — SE(3) IRC-RL with real DSA feedback

Ongoing research: Training a diffusion-based trajectory planner on large-scale SOFA-generated synthetic data, transferring to real vascular geometries — targeting to overcome clinical data scarcity.

Affiliations: Tongji University × PKU DLIB | 2026

System Architecture

stEVE simulation pipeline: vessel tree, instrument, SOFA engine, and fluoroscopy rendering.

Catheter navigation in patient-specific vascular anatomy — final frame of a clinical test episode.

血管介入手术机器人平台（stEVE）

Sun, 01 Mar 2026 00:00:00 +0000

miracle-techlink / GitHub stEVE 平台代码仓库 GitHub Blog: VLA 如何落地医疗机器人 miracle-techlink.github.io · 综述文章 Link

面向 AI 辅助血管腔内介入的完整研究平台，覆盖术前三维规划至术中 RL 引导导航全流程。

处理流程：

CTA → STL — TotalSegmentator 血管分割，VMTK 中心线提取
SOFA FEM 仿真 — Cosserat 梁导丝模型，真实血管形变仿真
RL 导航 — SAC/PPO 智能体，测地距离奖励 + 安全约束
双平面 DSA — DLT 三角测量 + Beer-Lambert X 射线投影，实现 2D-3D 配准
术中校正 — SE(3) IRC-RL，融合真实 DSA 反馈

进行中研究： 在大规模 SOFA 生成的合成数据上训练基于扩散模型的轨迹规划器，迁移至真实血管几何结构——目标是克服临床数据稀缺问题。

归属： 同济大学 × 北大 DLIB | 2026

系统架构

stEVE 仿真流水线：血管树、介入器械、SOFA 引擎与荧光透视渲染。

临床导航演示

患者特异性血管解剖结构中的导管导航——临床测试片段最终帧。

Robotic Arm Simulation Engine — Tongji × Siemens Industry 4.0 Lab

Thu, 01 Feb 2024 00:00:00 +0000

Three.js real-time robotic arm visualization — adjusting slider parameters updates the 3D model in real time.

A joint research project between Tongji University’s Sino-German Industry 4.0 Lab and Siemens, replacing legacy particle swarm optimization (PSO) with deep reinforcement learning for higher-precision robotic arm control.

Technical work:

DQN agent for joint trajectory optimization, replacing PSO baseline
Three.js 3D visualization — interactive slider-based parameter control, real-time model updates, importable industrial equipment scenes
SITP project certification completed

Key insight: After exposure to NVIDIA Isaac Sim, identified that the core need is not model import/visualization but custom algorithm interfaces and GPU-accelerated physics simulation — pivoted research direction accordingly.

Affiliations: Tongji University Sino-German Industry 4.0 Lab × Siemens | 2023.11 – 2024.02

机械臂仿真引擎 — 同济×西门子工业 4.0 实验室

Thu, 01 Feb 2024 00:00:00 +0000

引擎演示视频 · Bilibili Three.js 机械臂仿真引擎实时演示 Bilibili

Three.js 实时机械臂可视化 — 调节滑动条旋转参数，模型相应变化。

同济大学中德工业 4.0 实验室与西门子联合研究项目，用深度强化学习替代传统粒子群优化（PSO），实现更高精度的机械臂控制。

技术工作：

DQN 智能体 用于关节轨迹优化，替代 PSO 基线
Three.js 3D 可视化 — 滑动条交互式参数控制，实时模型更新，支持导入工业设备场景
SITP 项目结项认证 已完成

关键洞察： 接触 NVIDIA Isaac Sim 后，识别到核心需求并非模型导入/可视化，而是自定义算法接口与 GPU 加速物理仿真 — 据此调整了研究方向。

归属： 同济大学中德工业 4.0 实验室 × 西门子 | 2023.11 – 2024.02