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Open nowPosted 405 days ago

强化学习算法工程师（运控方向）

x2-robot/index203 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now强化学习算法工程师（运控方向）x2-robot/index · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. x2-robot/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 405 days ago

x2-robot/index median: 1 days open

The posting

1. 负责机器人领域的强化学习算法研究与开发，包括机械臂、灵巧手、机器人全身运动控制等核心技能训练； 2. 熟悉机器人端到端操作模型，研究强化学习在ACT、Diffusion Policy、OpenVLA等前沿VLA模型中的应用； 3. 基于Isaac Gym/Sim/Lab、MuJoCo等仿真平台，设计并实现机器人操作策略的强化学习训练框架，解决sim2real迁移问题； 4. 研究操作模型基于人类或者模型反馈的真机强化学习，提升模型的泛化性和精准性，进一步提升模型的能力； 5. 研究并开发模仿学习与强化学习相结合的算法，实现机器人复杂操作技能的快速学习和泛化； 6. 负责强化学习模型的蒸馏、优化和真机部署，确保算法在实际机器人系统中的稳定运行； 7. 跟踪机器人强化学习领域最新研究进展，推动算法创新和技术突破。

1. 具有计算机视觉、人工智能、机器人学、控制工程等相关专业背景，硕士及以上学历； 2. 熟练掌握主流深度学习框架，如PyTorch、TensorFlow、JAX，并具备优秀的编程能力； 3. 具备扎实的强化学习理论基础，熟悉PPO、SAC、TD3、DDPG等主流强化学习算法，有相关项目实践经验； 4. 有Isaac Gym/Sim/Lab、MuJoCo等机器人仿真平台使用经验，具备sim2real项目开发经验； 5. 了解机器人运动学、动力学建模，熟悉机器人操作（manipulation）或运动控制相关技术； 6. 了解机器人端到端操作模型（如ACT、Diffusion Policy、OpenVLA、π0等）及大模型在机器人领域的应用； 7. 有机械臂+灵巧手或足式机器人的强化学习/模仿学习项目经验者优先； 8. 具备分布式训练经验，能够处理大规模数据和模型训练，有过强化学习系统框架开发者优先； 11. 机器人学习、强化学习相关顶级会议论文发表经历或相关比赛获奖经历者优先考虑； 12. 具有技术热情、技术信仰以及较强的学习能力和问题解决能力。

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