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Open nowPosted 145 days ago

Humanoid Whole-Body Policy Learning算法实习生

agirobot/internrecruitment367 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowHumanoid Whole-Body Policy Learning算法实习生agirobot/internrecruitment · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on agirobot/internrecruitment's own form.

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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. agirobot/internrecruitment postings stay open a median of 2 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. 57.8%60 days
This job: posted 145 days ago

agirobot/internrecruitment median: 2 days open

The posting

  • 参与构建 task-conditioned 全身控制 pipeline，深度参与 motion retargeting、policy 训练、reward / curriculum 设计、大规模 ablation 实验等关键环节。
  • 复现并改进领域最新代表性工作（如 whole-body tracking、loco-manipulation、humanoid RL 等方向），在团队自有数据集与真实任务场景下验证、对比与迭代算法表现。
  • 参与真机数据采集、sim-to-real 部署与真机调试，搭建可视化分析工具，定位 sim-real gap 并推动闭环优化。

- CS / Robotics / 控制 / 自动化等相关专业在读硕士或博士。 - 扎实的 Python / PyTorch 工程能力，对机器人运动控制和具身操作（embodied manipulation）有清晰的理解。 - 完整的 RL 训练项目经验：至少独立完成过一段在仿真中训练 RL policy 的工作（从环境搭建、reward 设计到调参收敛全流程），humanoid / legged 方向优先。 - 熟悉人形机器人条件动作生成的相关方法，如text2motion，audio2motion等。 - 对人形机器人自主全身控制方向有持续的热情与投入；实习周期不少于 6 个月，每周到岗不少于 4 天（表现优秀者优先转校招）。 加分项 - 熟悉 Isaac Lab / Isaac Gym / MuJoCo 等仿真平台，有 humanoid 或 legged robot 的实操经验。 - 有真机调试或 sim-to-real 部署的实战经历。 - 接触过 motion retargeting（GMR / PHC / PHUMA 等）、动作编辑或人体动捕数据处理。 - 熟悉 SMPL / AMASS 等人体动作数据集。 - 在相关方向有学术投稿或发表（CoRL / RSS / ICRA / ICLR / NeurIPS 等优先）。 - 具备 C++ 工程能力，能参与 policy 的端侧部署与实时推理优化。

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