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Open nowPosted 143 days ago

VLA 动作优化算法实习生

x2-robot/91213080 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowVLA 动作优化算法实习生x2-robot/912130 · Beijing, 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 143 days ago

The posting

1. 参与 VLA / 具身大模型的动作优化研发，包括 action chunk 优化、轨迹平滑、动作重定时、速度自适应、延迟补偿与执行稳定性提升等； 2. 调研 VLA 动作加速、实时推理、speculative inference、temporal / spatial optimization 等前沿技术路线，并协助完成算法设计、实现与实验验证； 3. 参与构建 VLA 动作后处理与执行优化模块，提升模型输出动作在真实机器人上的连续性、可控性和鲁棒性； 4. 协助完成机器人数据处理、动作轨迹分析、模型训练 / 微调、离线评测与真机验证实验； 5. 参与设计动作质量评估指标，如轨迹平滑度、任务成功率、执行耗时、延迟、抖动、精细阶段失败率等，并输出实验报告与技术文档； 6. 与模型、数据、控制和工程团队协作，推动 VLA 模型从离线训练到真实机器人部署验证的闭环迭代。

1. 计算机、人工智能、机器人、自动化、电子信息等相关专业硕士及以上学历优先，优秀本科生也可考虑； 2. 熟练使用 Python，熟悉 PyTorch 等深度学习框架，具备良好的 Linux 开发与调试能力； 3. 熟悉深度学习、模仿学习、机器人学习或多模态模型基础，对 VLA、Transformer-based Policy、Diffusion Policy、Flow Matching 等方向有兴趣或实践经验； 4. 理解机器人动作表示与时序建模相关概念，如 action chunk、trajectory、waypoint、joint action、end-effector pose、轨迹插值、动作平滑等； 5. 对真实机器人系统中的动作执行问题有兴趣，愿意分析并解决延迟、抖动、动作不连续、速度-精度权衡、精细阶段失败等实际问题； 6. 具备较好的英文论文阅读能力，能够主动调研前沿工作，并转化为可验证的实验方案； 7. 具备良好的工程实现、实验分析和文档表达能力，能够在导师指导下推进模块开发与实验复盘。

加分项 1. 有 VLA、OpenVLA、π 系列模型、Diffusion Policy、ACT、RT-1 / RT-2、机器人模仿学习等相关项目经验； 2. 有动作优化、轨迹优化、MPC、速度规划、延迟补偿、控制后处理或实时推理优化经验； 3. 熟悉 ROS / ROS2、机械臂控制、相机 / 本体感知数据同步、多频率数据管线，有真实机器人系统调试经验者优先； 4. 有 CUDA / Triton / TensorRT / vLLM / 模型推理加速经验，或 VLA 实时部署实践经验； 5. 在具身智能、机器人学习、多模态大模型、强化学习、控制等方向有论文、开源项目或竞赛经历者优先。

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