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Open nowPosted 60 days ago

【实习】物理智能世界模型训练与真机部署实习生 -物理智能中心

aicarrier/522799209 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【实习】物理智能世界模型训练与真机部署实习生 -物理智能中心aicarrier/522799 · Shanghai, 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 60 days ago

The posting

1、参与物理智能世界模型的训练与优化，包括机器人交互数据建模、动作条件预测、轨迹生成、状态预测等方向。 2、协助构建和维护机器人训练数据流程，包括数据采集、清洗、标注、对齐、质量评估。 3、参与多模态机器人模型训练，包括视觉、语言、动作、状态、传感器信息等多源输入的建模与实验。 4、负责或协助完成模型在真机机器人上的部署与验证，包括推理链路搭建、控制接口适配、延迟优化、稳定性测试和实验记录。 5、参与机器人任务实验设计，例如抓取、放置、操作、导航、长程任务执行等，并分析模型在真实环境中的失败案例。 6、协助搭建仿真到现实的实验闭环，包括仿真环境配置、策略评估、真机复现、误差分析和迭代优化。 7、跟进物理智能、机器人基础模型、World Model、VLA/VLM、Diffusion Policy、Imitation Learning、RL 等相关前沿工作，并复现或验证关键方法。与算法、机器人、平台工程团队协作，将研究模型转化为可运行、可评估、可迭代的真机系统。

任职要求 1、计算机、人工智能、机器人、自动化、电子工程、机械工程、数学等相关专业本科高年级、硕士或博士在读。 2、熟悉 Python，具备良好的工程实现能力，能够使用 PyTorch / JAX / TensorFlow 中至少一种深度学习框架完成模型训练与调试。 3、了解机器人学习或具身智能相关基础概念，例如模仿学习、强化学习、行为克隆、轨迹预测、运动控制、状态估计等。 4、具备较强的深度学习基础，理解 Transformer、Diffusion Model、VLM/VLA、多模态表示学习、序列建模等至少一种相关技术。 5、具备较强的问题拆解能力和实验分析能力，能够独立阅读论文、设计实验、记录结果并进行复盘。 6、对真机实验有耐心，能够接受机器人系统调试中的不确定性，包括传感器误差、控制延迟、硬件故障、环境变化等实际问题。可长期实习者优先。

加分项 1、有机器人真机部署经验，使用过机械臂、移动机器人、灵巧手、双臂机器人或人形机器人等平台。 2、有 World Model、Video generation、Action-Conditioned Prediction、Latent Dynamics Model、Model-Based RL 等相关项目或论文经验。 3、有 VLA / VLM / LLM Agent / Robotics Foundation Model 相关经验。 4、熟悉仿真平台，如 MuJoCo、Isaac Gym / Isaac Sim、Habitat、SAPIEN、PyBullet、RoboSuite、ManiSkill 等。 5、有大规模模型训练经验，熟悉分布式训练、混合精度训练、数据并行、模型并行、训练加速、显存优化等。 6、有机器人数据集使用经验，例如 RLBench、BridgeData、Open X-Embodiment、DROID、CALVIN、LIBERO、RoboMimic 等。 7、有视觉感知或三维理解经验，例如 RGB-D、点云、目标检测、分割、姿态估计、3D reconstruction、SLAM 等。 8、具备较强的 C++ 能力，能够参与底层控制、实时通信或机器人系统集成开发。 9、在机器人、机器学习、计算机视觉、强化学习等方向有高质量论文、竞赛、开源项目或科研经历。

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