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Open nowPosted 492 days ago

强化学习算法工程师（具身大模型）

x2-robot/index203 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now强化学习算法工程师（具身大模型）x2-robot/index · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. x2-robot/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 492 days ago

x2-robot/index median: 1 days open

The posting

1. 机器人强化学习算法研究与开发： 负责前沿机器人强化学习算法的研究、设计和实现，重点方向包括：基于扩散模型/VLA的策略学习与强化学习微调、离线/off-policy RL（如IQL、CQL、RLPD等）、residual policy learning、以及长horizon任务的课程学习与稀疏奖励设计。 2. VLA大模型训练与优化： 负责Vision-Language-Action大模型（如RT-2、π0.5系列等）的训练、微调和推理优化，熟悉action chunking、多模态embedding提取等技术，提升模型在真实机器人操作任务上的性能。 3. 机器人强化学习数据与推理链路搭建： 负责真实机器人上RL在线数据收集pipeline的搭建，包括：实时推理部署、异步inference处理、trajectory数据的采集/存储/回放、奖励信号计算与标注，以及sim-to-real数据流的对接。 4. 机器人系统集成与部署： 负责将训练好的模型部署到真实机器人系统（如双臂桌面操作平台, 足式或轮式人形机器人等），熟悉ROS2通信架构、机器人运动学/动力学基础、传感器数据处理及实时控制链路，能够自主修改，搭建一个完整的推理采集，上传，训练的链路系统，并保障系统稳定运行与性能监控。 5. 仿真环境搭建与Sim2Real： 参与搭建和维护机器人仿真环境（如MuJoCo、Isaac Sim等），进行策略的仿真验证与sim-to-real迁移。 6. 技术文档撰写与分享： 撰写相关技术文档，复现和跟踪前沿论文（如PI的RL Token等），与团队进行技术交流和协作。

基础技能 1. 计算机科学基础： 扎实的计算机科学基础，包括数据结构、算法、操作系统、Linux系统管理等。 2. 编程能力： 熟练掌握Python，熟悉C++；具备多机多仓库协作开发经验。 深度学习框架： 熟练掌握PyTorch，具备分布式训练、混合精度训练等实际工程经验。

专业技能 1. 强化学习（RL）： 深入理解强化学习原理，熟悉on-policy（PPO）和off-policy（SAC、TD3、IQL、CQL）算法；了解offline RL、residual RL、diffusionRL等进阶方向；理解reward shaping、课程学习、UTD ratio调优等实践技巧。 2. 生成式策略模型： 熟悉Diffusion Policy、Flow Matching、Action Chunking Transformer等生成式机器人策略模型的原理与实现。 3. VLA/大模型： 理解Vision-Language-Action模型架构（Transformer backbone、多模态token化、autoregressive action decoding），熟悉预训练、微调流程。 4. 机器人系统： 熟悉ROS2通信框架，了解机械臂运动学、关节控制、遥操作数据采集（如ALOHA/ACT范式）等；有真实机器人调试经验者优先。 5. 仿真与Sim2Real： 有MuJoCo、Isaac Sim/Gym、或其他机器人仿真平台使用经验；了解domain randomization、sim-to-real迁移技术。 6. 计算机视觉： 具备CV基础，了解ViT、视觉编码器在embodied AI中的应用。

加分项 1. 在顶级学术会议（NeurIPS、ICML、ICLR、CoRL、RSS、ICRA等）或期刊发表过机器人学习相关论文。 2. 有真实机器人RL训练落地经验（非纯仿真）。 3. 熟悉多机协同开发工具链（如多仓库管理、远程开发、SSH tunnel调试等）。 4. 有开源项目贡献经验，或复现过知名robotics/RL论文。 5. 熟悉LeRobot、openpi等开源机器人学习框架。

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