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Open nowPosted 128 days ago

虚实对齐算法实习生

x2-robot/91213080 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now虚实对齐算法实习生x2-robot/912130 · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 128 days ago

The posting

1. 真实感仿真与建模 (Real2Sim)： 负责搭建和标定高保真的机器人物理仿真环境，深入执行器（如QDD准直驱电机）底层，通过系统辨识或数据驱动的方式（如训练 ActuatorNet 等神经网络模型）精准还原电机的真实动态与非线性特征。 2. 系统辨识与物理对齐： 负责机器人本体（关节、连杆、整机）的动力学参数辨识与传感器标定，确保仿真环境与真实机器人的物理特性高度对齐。 3. 强化学习落地 (Sim2Real)： 基于 Isaac Gym / Isaac Lab / MuJoCo 等仿真平台，设计并优化域随机化（Domain Randomization）、传感器噪声注入及课程学习（Curriculum Learning）策略，解决RL算法从仿真到真机的迁移难题。 4. 全链路闭环建设： 搭建“真机数据采集 — 仿真模型训练 — 效果评测对齐 — 真机验证”的模块化数据闭环管线，支撑算法的高效迭代与敏捷部署。 5. 前沿技术跟进： 追踪具身智能、仿真渲染、接触力学及机器人强化学习领域的最前沿进展（如 OpenAI 的仿真写实技术），推动底层算法创新。

1. 硕士或博士学历优先，计算机、自动化、机器人、机械电子、力学等相关专业。 2. 扎实的物理与控制底盘： 熟练掌握机器人运动学与动力学理论，精通系统辨识、参数估计与优化求解方法，对真实物理世界的力/位传递有深刻理解。 3. 出色的代码工程能力： 精通 Python 与 C++，具备良好的现代 C++ 编程习惯；熟练使用 PyTorch 或 JAX 等深度学习框架。 4. 仿真与算法经验： 熟悉至少一款主流机器人仿真平台或物理引擎（如 Isaac Lab, MuJoCo, PyBullet 等）；熟悉 PPO, SAC 等主流强化学习算法，理解基于数据的训练闭环。 5. 加分项（非硬性要求，满足其一即可极大加分）： * 有真机硬件（机械臂、四足/轮足机器人、灵巧手）的RL算法落地与调试经验，完整经历过 Sim2Real 闭环。 * 熟悉底层控制算法与架构，有 MPC、WBC 或 ROS 2 开发经验者优先。 * 有电机/执行器神经网络建模、接触力学建模或复杂系统辨识相关项目经验。 * 在机器人或 AI 领域顶级会议（如 ICRA, IROS, CoRL, RSS 等）发表过高水平论文，或在相关技术竞赛中取得优异成绩。 * 具备极强的技术热情、First-principle thinking（第一性原理思考）能力，以及迎难而上的问题拆解与Debug能力。

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