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Open nowPosted 114 days ago

【27届】强化学习算法工程师

x2-robot/91213080 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now【27届】强化学习算法工程师x2-robot/912130 · Shenzhen, Guangdong, Chinese Mainland

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Share of postings closed within
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  4. 18.1%14 days
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  6. 44.6%60 days
This job: posted 114 days ago

The posting

岗位简介： 让基础模型（VLA、WAM 等）在 SFT 之上通过 RL 持续自我提升——摆脱人工数采，靠机器人自主采集 + 在线强化，把真实操作任务的成功率、速度与精细度一路推高，并向多任务泛化、多机大规模训练演进。 1. 单任务：在 VLA/WAM 的 SFT 基础上用 RL 进一步提升成功率与速度，并靠自主/在线采集提升精细操作的数采效率，摆脱人工遥操作依赖，形成自改进闭环。 2. 多任务：训练跨任务泛化的通用 reward/feedback 模型（VLM-as-reward、RLT 类范式），减少逐任务奖励工程。 3. 多机：探索多机器人大规模 RL——分布式在线采集、fleet 数据聚合与异步训练。 4. 真机：以上都落到真实任务上生效，保障"推理采集 → 上传 → 训练 →数据回流 "链路稳定可监控。

核心职责： 1. RL 算法与前沿复现：复现改进 HIL-SERL/SERL、RLPD、IQL/CQL、residual RL 等；设计 SFT→RL 的 post-training 方案（reward shaping、课程学习、UTD 调优）。 2. VLA/WAM + RL 微调：负责基础模型（wall-oss-0.5, wall-wm）的 RL 微调与推理优化，复现RL Token、RECAP 等工作；研发通用奖励模型， 解决多任务奖励泛化问题。 3. 真机在线 RL 闭环（核心）：搭建实时/异步 inference、trajectory 采集/回放、reward 计算、HIL 接入的完整闭环，并扩展到多机规模。 4. 系统集成与 Sim2Real：基于 ROS2 部署到双臂/人形等真实平台；用 Isaac Lab 做仿真验证与 sim-to-real。

基础：毕业时间介于2026年9月至2027年8月的国内外高校的本科生、硕士生、博士生；扎实 CS 基础（数据结构/算法/OS/Linux）；熟练 Python 布式与混合精度训练经验；多机多仓库协作经验。

专业： 1. 深入理解 RL，熟悉 PPO / SAC / TD3 / IQL / CQL，了解 offline/residual/diffusion RL 与 reward shaping、课程学习、UTD 调优。 2. 熟悉 Diffusion Policy、Flow Matching、ACT 等生成式策略模型，理解 VLA/WAM 架构的预训练/微调流程。 3. 了解 world model / 基于预测的策略学习（如 world-model 和 RL 的结合方式）。 4. 熟悉 ROS2、机械臂运动学与遥操作数采；针对强化学习数据有完整的标注经验；有真机调试经验优先。 5. 有 Isaaclab 及 sim-to-real 经验；具备仿真中强化学习基础， 双臂、灵巧手操作优先。

加分项：顶会论文（NeurIPS/ICML/ICLR/CoRL/RSS/ICRA）；真机 模型 / 多机分布式 RL / 自主数采经验；熟悉LeRobot、openpi；复现过 HIL-SERL、RLPD、π 系列、RLT 等或有开源贡献。

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