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Open nowPosted 78 days ago

机器人运动控制工程师 — 强化学习

wcn7iq2vq1am/86371186 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now机器人运动控制工程师 — 强化学习wcn7iq2vq1am/863711 · Shanghai, 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. wcn7iq2vq1am/863711 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. 58.1%60 days
This job: posted 78 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

岗位方向 1.面向机械臂及轮式移动双臂机器人，开展强化学习运动控制算法研发与真机部署。 2.负责从任务建模、策略训练、Sim-to-Real 到真机调试的完整闭环，主要应用于高动态操作、移动操作和全身控制任务。 岗位职责 1.根据任务需求设计、训练、评估并部署机器人强化学习控制策略； 2.根据真机部署结果持续优化训练环境、策略及部署管线； 3.分析并解决 Sim-to-Real 问题，通过 Curriculum Learning、Domain / Dynamics Randomization、系统辨识、执行器建模和延迟建模等方法提升策略迁移能力； 4.负责 Policy 真机部署与调试，包括实时推理、控制接口、安全保护及性能优化； 5.基于仿真和真机实验分析策略问题，与硬件、电控、仿真及系统团队协作，持续提升策略性能和鲁棒性。

任职要求 1.具备两年及以上机器人强化学习算法开发经验，熟悉 PPO、SAC 等主流算法； 2.具备实际 RL 训练和调试经验，能够分析训练稳定性及策略性能问题； 3.熟悉至少一种机器人仿真 / 训练平台，如 Isaac Lab、MuJoCo、SAPIEN / ManiSkill 等； 4.具备扎实的机器人运动学、动力学及基本控制理论基础； 5.具备机器人 Policy 真机部署经验，能够分析和解决 Sim-to-Real、控制延迟、执行器动态、状态估计及策略稳定性等实际问题； 6.对机器人强化学习领域保持持续关注，能够主动跟踪并学习相关前沿技术。

加分项 1.有大规模并行训练、Distributed RL、System Identification 或 Actuator Modeling 经验； 2.熟悉 WBC、MPC、Operational Space Control、Impedance Control 等传统机器人控制方法； 3.有 Behavior Cloning、Policy Distillation 等模仿学习或策略蒸馏经验； 4.有基于真机数据进行策略优化、Real-world RL 或在线学习相关经验； 5.在机器人或强化学习相关会议发表过论文，如 CoRL、ICRA、RSS、NeurIPS、ICLR、ICML 等。

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