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Open nowPosted 163 days ago

蔚来AGI超星计划-面向车身运动控制（VMC）的物理AI与多场景重建强化学习研究

nio/campus867 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now蔚来AGI超星计划-面向车身运动控制（VMC）的物理AI与多场景重建强化学习研究nio/campus · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. nio/campus postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 163 days ago

nio/campus median: 1 days open

The posting

课题介绍 本课题面向车身运动控制（VMC）在复杂工况下的性能与安全问题，构建“参数辨识 + 物理仿真 + 强化学习 + 场景风险评估”的一体化控制框架。目标是在路面附着变化、载荷波动、非线性耦合和多执行器协同等条件下，实现更稳定、更可迁移的控制策略。相较传统“模型+规则+人工调参”方法，本课题重点研究可学习的补偿控制：在保留经典控制器主结构的基础上，引入前馈补偿与残差策略（residual policy），实现误差驱动的在线修正与实时优化。同时，课题建设场景驱动风险管控链路：围绕典型、极限、长尾和高风险场景，打通仿真、HIL 与实车验证流程，形成“场景生成—策略训练—风险评估—闭环验证”的完整体系，系统评估安全性、鲁棒性与泛化能力。 具体工作 1. 搭建多层级车身动力学仿真环境（简化模型到高保真模型）。 2. 研究关键参数在线/离线辨识方法（轮胎、质量分布、阻尼、附着系数等）。 3. 设计带物理与安全约束的强化学习策略，用于前馈补偿或残差控制。 4. 研究误差学习模块与经典 VMC 控制器的协同机制。 5. 构建 VMC 场景库与风险评估体系，支持风险分级和测试覆盖分析。 6. 研究 Sim2Real 一致性与鲁棒性，建立基于实测反馈的持续优化机制。 预期产出 1. 研究报告或高水平论文（强化学习、智能控制、车辆动力学、安全验证等方向）。 2. 一套面向 VMC 的学习控制框架：参数辨识、约束强化学习、风险评估、仿真闭环。 3. 高价值场景库：覆盖典型、极限、长尾和高风险工况。 4. 可在仿真与 HIL 验证、并具备实车迁移潜力的策略原型。 5. 可复用的“仿真-HIL-实车”场景与泛化评测框架。 6. 虚实闭环工具链：数据生成、场景挖掘、参数辨识、风险评估、策略优化。

  • 专业能力：
  • 具备强化学习、控制理论、车辆动力学或机器人学基础，熟悉PPO/SAC/Model-based RL等方法；
  • 数学基础扎实：系统动力学、最优控制、概率统计、优化方法；
  • 熟悉至少一种建模/仿真工具：Simulink、Simscape、CarSim、Isaac Sim、Mujoco等；
  • 具备Python或C++开发能力，熟悉PyTorch或TensorFlow；
  • 理解驾驶场景分析、风险建模、测试验证或数据闭环优化；
  • 加分项：
  • 有VMC、底盘控制、线控底盘或运动控制经验；
  • 有参数辨识、系统辨识、数字孪生或高保真动力学建模经验；
  • 有场景库建设、智能辅助驾驶测试验证、风险场景挖掘或长尾分析经验；
  • 有误差学习、内部模型（internal model）或生物启发控制相关研究背景；
  • 在NeurIPS、ICLR、ICRA、RSS、IV、ITSC、T-RO等发表论文；
  • 有Sim2Real、在线学习、持续学习、安全强化学习或端侧部署优化经验；
  • 硕士或博士在读，自动化、车辆工程、机器人、计算机、人工智能、控制科学与工程等相关专业；
  • 期望实习时长：6个月以上
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