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Open nowPosted 44 days ago

具身智能模型实验实习生

x2-robot/91213080 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now具身智能模型实验实习生x2-robot/912130 · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% 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. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 44 days ago

The posting

我们已经构建了一套具身智能 Benchmark，覆盖真实机器人任务、仿真任务、多样性设置、ID/OOD评测和多模态数据采集。现在希望招聘一位实习生，主要负责主流开源具身智能模型的复现、训练、评测和实验分析，帮助我们系统比较不同模型架构在真实机器人和仿真任务上的表现。

主要职责： 1. 调研并跑通主流开源具身智能/机器人操作模型，包括 VLA、WAM、Diffusion Policy、ACT、Transformer policy、BC/RL policy、多模态策略模型等方向。 2. 阅读模型论文和代码，梳理模型架构、输入输出格式、训练数据格式、action representation、推理流程和评测方式。 3. 将开源模型适配到我们的 benchmark 数据和任务接口上，包括图像、语言、状态、动作、轨迹等数据格式转换。 4. 执行模型训练、finetune、inference 和 evaluation 实验，维护实验配置、日志、checkpoint 和结果表格。 5. 对比不同模型在不同任务类型、数据量、泛化维度、ID/OOD评测和真实机器人任务下的表现。 6. 分析失败案例，整理模型在精细操作、可形变物体、流体/颗粒、长时序任务和真机部署中的能力边界。 7. 编写实验报告，输出模型复现笔记、架构分析文档和评测结果总结。

1. 本科高年级或硕士在读，计算机、机器人、人工智能、自动化等相关方向优先。 2. 熟悉 Python 和 PyTorch，能独立阅读、运行和修改深度学习代码。 3. 熟悉 Linux、Git、conda/docker、GPU训练环境，能处理常见依赖、显存、数据路径和单机/多机训练问题。 4. 对具身智能、机器人学习、多模态模型、VLA模型或 imitation learning 有兴趣。 5. 能读英文论文和开源代码，愿意深入理解模型架构，而不是只会运行脚本。 6. 有真实机器人实验经验，理解从数据采集、数据清洗、模型训练、推理部署到真机评测的完整流程。 7. 做事细致，能认真记录实验配置、结果、失败原因和复现实验步骤。

加分项 1. 跑过或读过 Diffusion Policy、ACT、OpenVLA、Openpi、WAM系列等机器人/具身模型代码。 2. 熟悉 Transformer、VLM/VLA、diffusion model、behavior cloning、trajectory prediction 等模型结构。 3. 有完整真机闭环经验，包括相机/机器人数据采集、episode整理、action对齐、模型训练、policy inference、真机部署和失败分析。 4. 有机械臂、灵巧手、移动操作平台、多相机系统或遥操作/示教数据采集经验。 5. 有仿真环境经验，如 Isaac Sim、MuJoCo、RoboSuite、RLBench 等。 6. 熟悉 wandb/tensorboard、实验管理、结果可视化和 ablation 分析。 7. 有较强debug能力，能定位代码、数据、环境、模型配置和真机部署问题。

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