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Open nowPosted 130 days ago

具身算法实习生（快慢系统）

x2-robot/91213080 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now具身算法实习生（快慢系统）x2-robot/912130 · Shenzhen, Guangdong, Chinese Mainland

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

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The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

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.

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 130 days ago

The posting

1. 负责面向家居、养老等高动态人机交互场景（如抱扶、护理、协作操作）的 System 1 / System 0 分层操作策略研发，构建低频语义规划（1–10 Hz）与高频行为生成（50–100 Hz）协同的统一框架，实现长短时序解耦与稳定闭环控制。 2. 设计并实现 System 1 层的操作语义建模能力，包括轨迹、接触模式与接触力等关键表示，并兼顾任务指令理解、视觉避障与多目标约束下的规划能力。 3. 研发 System 0 层高频控制策略，在关节空间生成稳定、连续且可控的操作行为，实现刚度调节、接触稳定与局部避障能力，并解决高频执行中的延迟、抖动与不连续问题。 4. 应用并拓展行为克隆、强化学习（RL-first paradigm）、生成式策略（Diffusion / Flow Matching）及元学习等方法，构建具备闭环执行、异常恢复与实时适应能力的操作策略。 5. 构建融合视觉、本体感知、力/触觉与语言的多模态具身模型（VLA / WAM），进行多模态信息感知、长-短时序任务目标规划、行为语义规划、运动轨迹生成等任务，并设计适配不同时间尺度的模型架构，实现从语义规划到动作生成的统一建模。 6. 推动算法在真实机器人系统中的部署与优化，参与硬件联调，定位并解决抖动、迟滞、接触不稳定等工程问题，构建推理机与执行机分离的低延迟系统架构，保证高频执行与低频推理的稳定协同。

1. 具备机器人学习、控制与多模态具身智能的系统认知； 2. 熟悉BC、RL、Diffusion/Flow Matching、VLA/WAM等前沿方法 3. 有真实机器人系统部署经验，熟悉推理延迟优化、异步执行架构或分层控制系统设计 4. 在具身智能或机器人学习领域（如ICRA, IROS, RSS, TRO, IJRR，RAL）有论文发表或高质量开源项目经验。

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