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Open nowPosted 88 days ago

【2027 届校招】运控算法工程师

nwd4iy9rd2s/campusofspiritai57 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【2027 届校招】运控算法工程师nwd4iy9rd2s/campusofspiritai · Beijing, Chinese Mainland

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Share of postings closed within
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  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 88 days ago

The posting

岗位职责 1. 基于强化学习的人形机器人（轮式/轮足/双足）运动控制算法研究与实现，包括行走、操作、跑步、跳跃等动态运动技能的训练与部署。 2. 设计并优化Sim-to-Real迁移策略，解决仿真与真实机器人之间的动力学差异（domain gap），针对机器人本体特性提出算法与机电协同设计的改进建议。 3. 构建强化学习训练框架（如Isaac Gym、Mujoco + RLlib等），探索强化学习与传统控制融合、大小脑算法融合。 4. 参与机器人运动学、动力学模型的搭建与仿真环境构建，建立基于强化学习的运动性能评估标准，与传统控制方法性能做对比。 5. 跟踪前沿强化学习算法（如PPO、SAC、TD3、Dropout Q-learning等）及机器人运动控制领域最新成果，进行技术文献调研与原型实现。

职位要求 1. 硕士及以上学历，有强化学习在实际机器人系统上的部署经验、有Sim-to-Real迁移（域随机化、系统辨识、教师-学生策略等）实际项目经验。 2. 深入理解强化学习基础理论，熟悉MDP建模、策略梯度、价值函数估计、探索-利用权衡等核心概念。 3. 熟练掌握至少一种主流强化学习框架（RLlib、Stable-Baselines3、Acme、TensorFlow Agents等），并有实际训练与调优经验。 4. 熟悉机器人学与传统运控（运控规划、运动学、动力学等），与仿真环境（Mujoco、PyBullet、Isaac Gym等）进行交互，设计状态空间、动作空间与奖励函数。 5. 熟悉ROS2框架，掌握最优化与数值计算方法，能够理解强化学习算法中的梯度估计、约束优化等问题。 6. 熟悉NVIDIA Isaac Sim/Isaac Gym、MuJoCo等高性能仿真训练平台，熟练使用C++、Python，熟悉Eigen、NumPy等科学计算库，具备良好的工程化代码能力。 7. 熟悉常用传感器（IMU、力矩/力传感器、编码器等）及其在强化学习观测空间中的建模方法，了解状态估计与滤波技术。 8. 保持对具身智能前沿技术的持续关注，有技术热情和技术审美，敢于创新，追求卓越。 优先条件 1. 具有人形机器人、四足机器人或其他复杂多体系统的强化学习运动控制经验者优先。 2. 在相关领域顶会（CoRL、ICRA、RSS、NeurIPS等）发表过强化学习与机器人控制结合论文者优先。

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