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Open nowPosted 32 days ago

机器人运动智能工程师

agirobot/internrecruitment362 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now机器人运动智能工程师agirobot/internrecruitment · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on agirobot/internrecruitment's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

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 32 days ago

The posting

参与灵巧手操作的核心算法研发，覆盖仿真建模、运动控制、强化学习与Sim-to-Real全链路，在真实机器人平台上积累可落地的具身智能经验。 主要职责： 1. 负责 MuJoCo 或 Isaac Gym/Lab 环境下灵巧手/机械臂模型的搭建、参数对齐及物理特性调优。 2. 灵巧手/机械臂运动学正逆解、动力学分析，或参与强化学习/模仿学习策略的训练与测试。 3. 参与Sim-to-Real全流程：处理数据、调试控制逻辑、优化迁移效果，缩小仿真与真机差距 4. 开发遥操作系统，优化重定向算法，支持高效数据采集与技能泛化 5. 跟踪机器人操作领域最新成果，完成关键论文的复现与改进 职位要求： 1. 控制、机器人、自动化、计算机等相关专业在读硕士或博士研究生。 2. 熟悉机器人学基本原理（坐标变换、FK/IK、动力学方程）。 3. 具备 Python， C++ 编程能力，熟悉ros2基本架构。 4. 熟悉 MuJoCo、Isaac Gym/Lab 或 PyBullet 其中至少一种物理仿真器。 5. 了解基础的数值优化（如 QP 求解）以及经典控制理论（PID、LQR）。 加分项： 1. 有强化学习/模仿学习项目经验 2. 有灵巧手、多指操作或接触丰富任务经验 3. 有Sim-to-Real落地经验 4. 在机器人或机器学习顶会发表过论文 实习收获： 1. 接触真实的机器人算法工程化流程，了解“算法→真机→落地”完整链路 2. 资深工程师一对一指导，可开具实习证明 3. 优秀实习生有转正机会

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