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Open nowPosted 76 days ago

运动控制算法工程师

n8r2cr07gk/92063474 open roles

Where
Chengdu, Sichuan, Chinese Mainland
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Your applicationOpen now运动控制算法工程师n8r2cr07gk/920634 · Chengdu, Sichuan, 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.

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 76 days ago

The posting

1、运动控制算法研发迭代：负责机器人运动控制算法研发与优化，基于强化学习（RL）开展整机步态控制、姿态平衡、轨迹跟踪、运动规划、避障决策等核心算法设计与迭代； 2、强化学习策略训练与调优：独立完成强化学习智能体建模、奖励函数设计、策略网络搭建、超参调优、训练流程迭代，解决训练不收敛、运动抖动、步态不稳定、泛化能力弱等核心问题； 3、仿真环境搭建与迁移：基于UE/Isaac/Gazebo等仿真平台搭建机器人运动仿真训练环境，完成场景、动力学、传感器噪声仿真配置，实现仿真高效训练与虚实迁移、仿真到真机的策略适配优化； 4、真机落地与效果优化：负责训练策略的真机部署与调试，针对真实物理误差、环境扰动、动力学偏差做算法适配与鲁棒性优化，持续提升机器人运动速度、平稳性、抗干扰能力与复杂场景适应性； 5、前沿技术预研与沉淀：跟进强化学习、机器人学习、仿人运动、智能体控制、虚实融合等前沿技术，开展技术预研与方案创新，沉淀标准化训练流程、控制算法模块与技术文档； 6、跨团队协同落地：配合硬件、嵌入式、工程团队完成算法联调、功能迭代与版本交付，定位解决运动控制过程中的各类算法问题，保障项目稳定落地。

1、学历专业：硕士及以上学历，自动化、人工智能、机器人工程、控制科学与工程、计算机、力学等相关专业； 2、专业基础：扎实掌握机器学习、深度学习、强化学习核心理论，熟悉机器人运动学、动力学、自动控制原理，掌握经典控制与学习式控制相结合的算法思路； 3、技术能力：熟练使用Python、PyTorch/TensorFlow，精通PPO、SAC、DQN等主流强化学习算法；熟悉机器人运动规划、状态估计、姿态控制相关技术方案； 4、仿真与落地能力：熟悉Isaac、UE、Gazebo任意一种机器人仿真环境，具备强化学习运动策略仿真训练、虚实迁移、真机调试经验者优先； 5、项目经验：有机器人强化学习控制、步态优化、智能体决策等相关科研或项目经验者优先，有相关顶会论文、竞赛经历者加分； 6、综合素质：具备良好的数学建模、实验设计与问题分析能力，逻辑严谨，自驱力强；善于团队协作，能够高效推进算法迭代与工程落地，对机器人学习与智能运动控制有长期深耕意愿。

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