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Open nowPosted 72 days ago

【27届校招】机器人运动控制算法工程师

flexivrobotics/97193226 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【27届校招】机器人运动控制算法工程师flexivrobotics/971932 · Shanghai, Chinese Mainland

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  6. 45.1%60 days
This job: posted 72 days ago

The posting

团队介绍： 我们是一支面向机器人应用落地与规模化量产的具身智能后训练团队，负责将前沿模型能力转化为能够在真实场景中稳定运行、持续创造客户价值的机器人产品。团队以实际业务需求和市场反馈驱动技术迭代，贯通数据采集、模型训练、真机评测、部署优化与量产交付全流程。在这里，研发同学不仅能探索 VLA、模仿学习和强化学习等前沿方向，还能在复杂真实环境中持续打磨技术，通过客户使用效果验证判断、调整方向，最终亲手推动算法从实验走向产品、从技术成果走向规模化价值。

岗位职责： 1.参与机器人整机运动控制器的核心算法研发，协助完成从任务空间到关节空间指令的映射与底层控制参考生成； 2.在导师指导下，学习并实现合适的控制框架，包括但不限于分层全身控制、基于优化的MPC/轨迹优化（如aligator、cuRobo等），以及学习与传统控制融合的方案； 3.协助处理控制器中的轨迹跟踪精度、运动学/动力学约束（关节位置/速度/加速度/力矩限制、奇异规避等）及自碰撞与环境碰撞等问题； 4.参与算法的仿真验证与真机调试，协助完成从仿真到实机（Sim-to-Real）的迁移与性能调优，支撑抓取、放置、装配等场景下动作的稳定执行。

1.2027届应届生及毕业3年以内的往届毕业生，本科及以上学历，机器人、自动化、控制、机械、数学等相关专业，有扎实优化/控制理论背景者优先； 2.具备扎实的机器人运动学与动力学基础，了解正/逆运动学、雅可比矩阵、动力学建模（拉格朗日/牛顿-欧拉等）； 了解数值优化基础，熟悉QP/NLP/凸优化，了解DDP/iLQR、SQP等轨迹优化方法及常用求解器（如OSQP、ProxQP等）； 3.熟悉常用机器人仿真工具（如Isaac Sim、MuJoCo、Bullet、Pinocchio等），有算法验证或Sim-to-Real迁移相关课题或实习经验者优先； 4.具备扎实的算法实现能力，能使用C++/Python实现算法原型并完成仿真验证； 5.有避障规划、碰撞检测、距离场/SDF相关项目或实习经验者优先； 6.有双臂+轮式移动操作整机或全身控制开发实习经验者优先； 7.有learning-based运动规划或控制经验（如基于强化学习的力控策略、MPNet、可微优化/可微MPC、diffusion policy等）者优先； 8.有力/力矩控制、阻抗/导纳控制等接触式作业控制实习经验，或熟悉柔性/非刚性物体操作者优先； 9.在ICRA/IROS/RSS/CoRL等顶会发表过相关论文，或有RoboMaster/RoboCup等机器人赛事获奖经历。

发展规划： 校招生入职即配备资深专业导师，提供技术、工程方法与职业发展的一对一全程指导，并结合个人特点定制清晰成长路径。 你将参与对接真实客户需求、机器人落地场景与量产交付的核心业务任务，完整覆盖从需求分析、方案设计到产品迭代的全流程，在市场反馈中同步提升技术深度、工程能力与业务判断力。 团队鼓励新人主动担当、快速试错、输出技术方案，让优质想法切实落地为产品能力。经过 1-2 年实践培养，能力突出者有机会成长为细分技术方向的负责人或核心骨干，独立牵头关键技术 / 业务模块的规划与迭代，协调资源推动落地并对结果负责，实现从校园新人到技术骨干乃至年轻负责人的快速成长。

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