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Open nowPosted 451 days ago

ego算法实习生

x2-robot/91213080 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowego算法实习生x2-robot/912130 · Beijing, Chinese Mainland

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  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 451 days ago

The posting

【核心任务】 负责基于现有模型开展 mid-training / post-training 实验，评估 Ego 数据是否能够提升机器人任务表现、提高训练效率、增强泛化能力，或减少后训练阶段对真机数据的需求。 【具体职责】 1.配合模型团队整理可用于训练的 ego 数据、UMI 数据、遥操作数据和真机数据。 2.设计并执行 mid-training / post-training 对照实验，例如 ego + real robot、ego + UMI、UMI + real robot 等组合。 3.比较不同数据比例下的训练效果，分析成功率、收敛速度、泛化能力和真机数据需求变化。 4.维护训练配置、实验日志、模型 checkpoint、评估结果和 ablation 表格。 5.协助建立 action benchmark 和 post-train 任务评估体系。 6.输出阶段性实验报告，帮助团队判断 ego 数据在不同训练阶段的价值。

【必须要求】 1.熟悉 PyTorch 或其他深度学习框架，能够独立运行训练和评估代码。 2.有基本的机器人学习、模仿学习、强化学习、多模态学习或视觉模型训练经验。 3.能够设计基本 ablation 实验，理解 baseline、control group、变量控制和指标对比。 4.能够分析训练日志，例如 loss、success rate、evaluation curve、overfitting、data mixture ratio 等。 5.熟悉 Linux、Git、conda/docker、服务器训练环境。 【加分项】 1.有 imitation learning、diffusion policy、VLA / VLM、robot policy finetuning 经验。 2.熟悉 LeRobot、OpenVLA、Octo、RT-1 / RT-2、ACT、Diffusion Policy 等框架或方法。 3.有真机机器人数据训练或仿真实验经验。 4.有多数据源混合训练经验，例如 real robot + human video、robot data + web video、UMI + teleop。 5.有 benchmark 设计和实验报告写作经验。

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