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Open nowPosted 67 days ago

【实习】全身控制-强化学习算法工程师

flexivrobotics/97193226 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【实习】全身控制-强化学习算法工程师flexivrobotics/971932 · Shanghai, Chinese Mainland

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Share of postings closed within
  1. 2.1%1 day
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  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 67 days ago

The posting

职位描述： 强化学习；人形机器人；全身控制；Sim2Real；运动控制 岗位职责： 1. 研发基于强化学习（Reinforcement Learning）的机器人全身运动控制算法，开展移动操作机器人全身控制方向的创新研究； 2. 针对机器人全身协调控制等任务，设计并优化强化学习训练框架，实现从机器人状态感知到全身关节控制的策略生成与实时推理，解决运动稳定性、泛化能力及控制精度等核心问题； 3. 搭建高保真机器人仿真训练环境，基于Isaac Gym、Isaac Sim、MuJoCo等平台开展大规模并行训练，研究Domain Randomization、System Identification、Residual Learning等Sim2Real关键技术，实现强化学习策略向真实机器人高效迁移。 4. 参与机器人控制数据采集、奖励函数设计、策略训练、模型评测及真机部署的全流程开发，持续优化机器人运动性能与控制稳定性； 5. 跟踪CoRL、RSS、ICRA、IROS、NeurIPS、ICLR等国际顶会及开源社区在机器人强化学习、Locomotion、Whole-body Control等方向的最新研究进展。 6. 总结研究成果，撰写技术文档、专利及学术论文。

1. 在校大学生，计算机、人工智能、机器人、自动化、控制、数学等相关专业，本科及以上学历。 2.精通Python编程，熟悉PyTorch等深度学习框架，具备良好的代码工程能力。 3. 掌握强化学习基本原理，熟悉PPO、SAC、TD3、DDPG等主流强化学习算法，具有机器人强化学习实践经验。 4. 熟悉机器人运动学、动力学及控制理论，理解Legged Robot、人形机器人或机械臂运动控制基本原理。 5. 具有Isaac Gym、Isaac Sim、MuJoCo、Genesis、Gazebo等机器人仿真平台使用经验，能够完成强化学习训练与评测。 6. 具有真实机器人平台调试经验，能够参与强化学习策略部署、参数优化及真机验证。

加分项： 1. 在CoRL、RSS、ICRA、IROS、NeurIPS、ICLR、CVPR等国际会议或期刊发表过机器人控制、强化学习或具身智能相关论文； 2. 熟悉机器人全身控制（Whole-Body Control, WBC）、Operational Space Control（OSC）、QP求解器等传统控制算法，并具备RL与经典控制融合经验； 3. 熟悉Isaac Lab、Legged Gym、RSL-RL、RL Games、Humanoid Bench等强化学习训练框架； 4. 具备Sim2Real相关经验，熟悉Domain Randomization、System Identification、Residual RL、Teacher-Student Distillation等策略迁移方法； 5. 具有机器人感知、多传感器融合（IMU、力传感器、视觉等）或运动规划相关经验； 6. 熟悉GPU并行训练、大规模强化学习系统及分布式训练框架，具备强化学习算法性能优化经验。

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