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Open nowPosted 192 days ago

loco-manipulation（高动态物体交互方向）

agirobot/referral515 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowloco-manipulation（高动态物体交互方向）agirobot/referral · 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. agirobot/referral postings stay open a median of 1 days.

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 192 days ago

agirobot/referral median: 1 days open

The posting

面向机器人与高动态物体交互场景，负责全身协同控制算法的研发与落地。参与构建融合移动、操作、平衡与恢复能力的一体化控制系统，推动机器人在复杂动态环境中的感知、决策与执行能力持续提升。 岗位职责 负责机器人 loco-manipulation 算法的设计、开发与优化，面向高动态交互任务构建全身协同控制能力； 研究并实现基于强化学习（RL）、模仿学习（IL）、运动先验（Motion Prior）等方法的whole body control 协同策略，提升机器人在动态任务中的时序控制、操作精度、鲁棒性与全身协调能力； 基于Isaac Lab、MuJoCo等仿真平台搭建和维护训练与评测环境，开发和测试机器人运动、操作及动态交互任务； 研究并实现面向复杂动态任务的策略学习方法，包括但不限于 motion imitation、motion tracking、motion matching、task-conditioned policy learning、curriculum learning 以及 sim-to-real 导向的训练与优化策略； 跟踪机器人运动控制、强化学习、模仿学习及动态交互领域前沿研究进展，如 Hitter、AMP、SONIC、BeyondMimic、SMASH 等，并结合业务需求完成算法复现、评估与工程落地； 参与机器人感知—决策—控制闭环系统的接口设计与联调，支持目标状态估计、运动预测、任务规划与控制策略的系统集成； 支持算法在真实机器人平台上的部署、调试与性能优化，分析并解决控制延迟、状态噪声、动力学失配、接触不稳定、策略退化等问题，推进算法稳定迁移到真实硬件。

机器人、计算机、自动化、人工智能、控制科学与工程等相关专业硕士及以上学历，博士优先； 具备扎实的机器人学基础，熟悉运动控制、刚体动力学、接触动力学、轨迹优化、最优控制、强化学习或模仿学习中的一个或多个方向； 熟练掌握至少一种主流机器人仿真平台，如 Isaac Gym、Isaac Lab、MuJoCo等，具备机器人运动控制或操作任务的建模、训练与评估经验； 精通 Python 或 C++，熟悉 PyTorch 等深度学习框架，具备良好的工程实现、问题分析与调试能力； 具备基于深度强化学习或模仿学习进行机器人 locomotion 或 loco-manipulation 策略开发的实际经验，能够独立完成环境搭建、观测与奖励设计、训练调参与策略评估； 熟悉 PPO / SAC / TD3 等算法； 具备良好的科研能力和英文文献阅读能力，能够快速理解、复现并评估前沿算法，并推动其工程化应用； 具备较强的沟通协作能力、自驱力和结果导向意识，能够与感知、控制、软件、硬件团队高效协作。 加分项 具备 humanoid、legged robot、dual-arm robot、mobile manipulator 等相关项目经验； 熟悉全身控制、末端操作、动作模仿、运动先验、动作生成、动作重定向等相关方向； 有高动态交互任务经验，如动态抓取、目标拦截、快速目标跟踪、移动操作或接触型交互任务； 熟悉 sim-to-real 相关方法，包括 domain randomization、噪声建模、系统辨识、策略鲁棒性优化等； 具备真实机器人部署经验，能够完成算法上线、实验验证、日志分析与问题定位； 有大规模并行强化学习训练经验，熟悉高吞吐训练框架与训练稳定性优化； 有顶会论文、开源项目贡献或完整算法落地经验者优先。

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