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Open nowPosted 518 days ago

机器人算法工程师-端到端方向

tarsrobot/index110 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now机器人算法工程师-端到端方向tarsrobot/index · Shanghai, Chinese Mainland

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11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 518 days ago

The posting

1. 端到端移动和操作模型研发： - 设计并实现高性能、高鲁棒性的端到端模型架构（如基于 BEV-Transformer, Diffusion Policy, 世界模型, 多模态大模型等），应用于机器人的感知到动作的端到端移动及操作。 - 研发面向机器人时序决策的行为生成模型，应用模仿学习 (IL)、离线/在线强化学习 (RL)、行为克隆 (BC)、扩散模型 (Diffusion Models) 等技术。 - 优化模型的时序推理能力、场景理解深度与长程动作一致性，提升在动态、交互、不确定性环境下的决策智能性和任务成功率。 2. 大规模数据闭环构建与驱动： - 设计并搭建高效的大规模机器人多模态数据（感知、规划、控制、任务）采集、存储与管理平台。 - 构建问题驱动的数据闭环系统，实现基于场景挖掘、失败案例分析、主动学习的数据自动筛选、标注增强与策略迭代优化。 - 开发分布式训练框架与高效的数据加载/增强策略，加速模型训练与实验迭代。 3. 模型部署与性能优化： - 负责端到端模型在边缘计算平台（如机器人嵌入式系统）的高效部署与优化。 - 持续监控与分析模型在仿真与实机环境中的性能表现。 4. 系统集成与效果验证： - 设计并执行全面的评估方案（仿真大规模测试、实机场景测试），量化模型在导航、避障、交互、复杂操作任务（如抓取、装配） 上的性能（成功率、效率、安全性、舒适性），并驱动模型持续改进。

1. 端到端模型核心能力： - 深入理解并掌握端到端模型的核心技术，精通至少一种主流架构范式的原理、实现与调优。 - 深入理解行为生成模型的核心算法（模仿学习 IL/BC、强化学习 RL 、扩散模型 Diffusion Models 在端到端中的应用）。 2. AI工程化能力： - 具备构建完整AI模型研发-部署的能力，包括大规模数据处理与管理、分布式模型训练框架的使用与优化、模型轻量化与加速技术 (知识蒸馏、剪枝、量化) 的原理与实践、模型部署工具链的使用经验 3. 机器人学基础与工程能力： - 熟练掌握 Python，具备扎实的编程基础和算法实现能力。 - 熟悉 Linux 开发环境，具备良好的软件工程实践。 - 了解多传感器（如 Camera, LiDAR, Radar, IMU）的基本原理与数据特性。 4. 加分项： - 具有将端到端算法成功应用于真实机器人平台的实际项目经验，并取得可验证的落地效果。 - 深入参与过大规模机器人数据闭环（Data-Centric AI）系统的设计、构建或优化，显著提升了模型性能。。 - 在机器人领域顶级会议/期刊（如SR/IJRR/RSS/T-RO/RA-L/JFR/ICRA/IROS等）发表过端到端移动或者操作相关的研究论文。 - 在权威机器人竞赛（如 RoboMaster, RoboCup等）中取得优异成绩。

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