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Open nowPosted 46 days ago

末端执行器设计优化算法科学家（仿真与强化学习方向）

wcn7iq2vq1am/86371186 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now末端执行器设计优化算法科学家（仿真与强化学习方向）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 46 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

1.负责机器人末端执行器设计优化方向的算法研发，面向装配、工具使用、精细插接、旋拧紧固等复杂操作任务，建立任务驱动、可量化、可迭代的设计优化方法。 建立末端执行器参数化建模与仿真评估框架，对机构构型、自由度配置、尺寸参数、驱动与传动方案、接触界面及传感器布局等关键要素进行联合建模、搜索和优化。 2.研究末端执行器形态与操作策略的协同优化，结合强化学习、模仿学习、轨迹优化或接触控制等方法，评估不同设计对精细操作、接触力调节及多阶段任务执行能力的影响 3.结合运动学与动力学建模、接触分析、大规模仿真及任务与场景生成，构建覆盖对准、插入、旋拧、工具操控等关键环节的评测任务，区分末端执行器设计瓶颈与策略能力不足 4.建立面向复杂操作的指标体系，评估任务成功率、操作精度、接触力可控性、对位误差容忍度、工具操控稳定性和任务效率，并在负载、自重、可靠性、成本与可制造性等约束下优化设计参数 5.与机械、硬件、控制、感知和操作算法团队协作，将算法评估结果转化为末端执行器设计需求、参数建议及方案取舍依据，通过样机和真实任务验证推动设计迭代；聚焦末端执行器，不承担机械臂、腰部、底盘等身体构型优化 6.建设可复用的设计优化算法、仿真资产、策略训练与自动化评测工具链，形成“任务需求—算法建模—设计优化—样机验证”的迭代闭环

1.博士学历，机器人、人工智能、计算机、控制、自动化、机械工程或相关专业，具备扎实的算法研究与实验能力。 2.熟悉 MuJoCo、Isaac Lab / Isaac Sim 等至少一种机器人仿真平台，具备独立搭建末端执行器及复杂操作任务仿真环境的能力，能够完成机构与接触建模、物体和工具模型导入、任务场景与操作约束配置、训练评测接口开发及仿真参数校准 3.具备夹爪、灵巧手、机械手、工具型执行器等末端执行器相关的建模、仿真、设计优化或操作算法研究经验，能够分析结构参数、接触条件与任务能力之间的关系 4.熟悉机器人运动学与动力学、机构学和接触力学，理解驱动与传动、结构刚度、负载、尺寸及制造工艺等工程约束，能够与机械团队共同定义设计变量和优化边界 5.熟悉参数化建模、数值优化、黑箱优化、多目标优化或自动化设计优化中的至少一个方向，能够独立搭建“建模—搜索—仿真评估—实验验证”的算法流程 6.熟悉复杂操作策略学习、轨迹优化或接触控制等方法中的至少一个方向，能够将其用于装配、工具使用、精细插接等任务，以及末端执行器设计方案的能力评估 7.熟悉 Python/C++ 及常用机器人仿真、优化或机器学习工具，具备较强的工程实现能力，能够搭建自动化实验流程并分析大规模实验数据 8.具备独立研究与跨学科协作能力，能够设计可复现的对照实验，分析仿真与真实系统的差异，并在算法指标、机械性能、控制复杂度、可靠性与成本之间作出清晰权衡

加分项: 1.有灵巧手、多指夹爪、自适应夹爪、柔性执行器、欠驱动机构、变刚度机构或工具型执行器相关研究与项目经验。 2.有触觉传感器的研发、选型、标定、测试或集成经验，理解其工作原理与性能边界 3.有触觉、力觉、视觉或多模态传感与末端执行器一体化设计经验 4.有装配、工具使用、精细插接、旋拧紧固、手内重定向等接触丰富操作任务的策略学习、规划或力控经验 5.有末端执行器形态与策略协同优化、多目标优化、机构优化、自动化设计、大规模仿真或 sim-to-real 经验 6.有与机械及硬件团队协作完成样机测试、真实任务验证、可靠性分析或设计迭代的经验 7.在机器人、机器人学习、设计优化或控制等相关顶级会议、期刊发表过高质量论文，或有高质量开源项目 8.有从 0 到 1 建设末端执行器设计优化算法、工具链或评估体系的经验 9.有使用 GPT 等前沿大模型及 AI 编程工具辅助设计的实践经验，能够将其用于设计方案探索、参数化建模、仿真脚本开发和实验结果分析，加速设计优化与迭代，并通过仿真或实验验证生成结果。

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