SR-Platform：一种基于自然语言的机器人仿真环境合成代理管道
SR-Platform: An Agentic Pipeline for Natural Language-Driven Robot Simulation Environment Synthesis
AI总结：
SR-Platform通过代理系统将自然语言描述转化为可执行的MuJoCo仿真环境，显著降低手动创建多样机器人训练环境的工作量，实现从普通英文提示在一分钟内生成可执行场景。
摘要：
生成机器人仿真环境仍然是基于模拟的机器人学习中的主要瓶颈。构建一个训练就绪的MuJoCo场景通常需要在3D资产建模、MJCF规范、空间布局、避障和机器人模型整合方面的专业知识。我们提出了SR-Platform，一个已部署的代理系统，能够将自由形式的自然语言描述转换为可执行且物理有效的MuJoCo环境。SR-Platform将场景合成分解为四个阶段：一个基于LLM的协调器，将用户意图转换为结构化的场景计划；一个资产锻造器，通过LLM到CadQuery的合成检索缓存资产或生成新的3D几何体；一个布局建筑师，分配物体姿态并验证工业约束；以及一个桥接层，将最终的MJCF场景组装在一起并合并所选的机器人模型。该系统作为九个服务的Docker堆栈部署，具有WebSocket进度流、MinIO背的网格存储、Qdrant基于的语义资产检索、Redis作业状态和InfluxDB遥测。使用30天的生产遥测数据，覆盖611次成功的LLM调用，SR-Platform生成五物体场景的中位端到端延迟约为50秒，而缓存加速的场景在30-40秒内完成。资产锻造器显示有11.3%的首次尝试重试率，具有自动恢复，缓存资产检索消除了对之前生成物体类型的每个对象LLM调用。这些结果表明，代理场景合成可以减少创建多样化机器人训练环境所需的人工努力，使用户能够在一分钟内从普通英文提示生成可执行的MuJoCo场景。
英文摘要：
Generating robot simulation environments remains a major bottleneck in simulation-based robot learning. Constructing a training-ready MuJoCo scene typically requires expertise in 3D asset modeling, MJCF specification, spatial layout, collision avoidance, and robot-model integration. We present SR-Platform, a production-deployed agentic system that converts free-form natural language descriptions into executable, physically valid MuJoCo environments. SR-Platform decomposes scene synthesis into four stages: an LLM-based orchestrator that converts user intent into a structured scene plan; an asset forge that retrieves cached assets or generates new 3D geometry through LLM-to-CadQuery synthesis; a layout architect that assigns object poses and verifies industrial constraints; and a bridge layer that assembles the final MJCF scene and merges the selected robot model. The system is deployed as a nine-service Docker stack with WebSocket progress streaming, MinIO-backed mesh storage, Qdrant-based semantic asset retrieval, Redis job state, and InfluxDB telemetry. Using 30 days of production telemetry covering 611 successful LLM calls, SR-Platform generates five-object scenes with a median end-to-end latency of approximately 50 s, while cache-accelerated scenes complete in approximately 30-40 s. The asset forge shows an 11.3% first-attempt retry rate with automatic recovery, and cached asset retrieval removes per-object LLM calls for previously generated object types. These results show that agentic scene synthesis can reduce the manual effort required to create diverse robot training environments, enabling users to produce executable MuJoCo scenes from plain English prompts in under one minute.
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