New Ops for physical AI: annotate generate replay convert

[1van2ha0]

添加算子：annotate_demos，generate_dataset，replay_randomized，convert_to_lerobot，支持从种子数据到可训练的lerobot数据的批量化处理
更新了对应的doc文档，为对应算子添加了单元测试与使用示例

[gemini-code-assist]

Summary of Changes

Hello @1van2ha0, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly expands the data processing capabilities for physical AI by integrating with Isaac Lab and supporting the LeRobot dataset format. It introduces a suite of new mapper operators that cover the entire pipeline from annotating raw robot demonstrations, generating synthetic mimic datasets, replaying and recording randomized simulations, to converting the processed data into a standardized format suitable for training robotic agents. These additions streamline the workflow for researchers and developers working with simulated robotic data, enhancing the efficiency and versatility of the data processing framework.

Highlights

• New Isaac Lab Integration: Four new mapper operators (annotate_demos, generate_dataset, replay_demos_randomized, convert_to_lerobot) have been added to facilitate physical AI data processing using Isaac Lab. These operators are designed to work with GPU acceleration and require actor restarts for clean Isaac Sim states.
• Automated Demonstration Annotation: The AnnotateDemosMapper enables automatic annotation of robot demonstration episodes by replaying them in an Isaac Sim environment, detecting subtask completions, and exporting annotated HDF5 files.
• Mimic Dataset Generation: The GenerateDatasetMapper allows for the creation of new mimic datasets within Isaac Lab simulation environments, using existing annotated episodes as a source.
• Randomized Demonstration Replay with Video Recording: The ReplayDemosRandomizedMapper supports replaying demonstrations with visual randomization and recording RGB and depth videos, providing valuable visual data for analysis and training.
• LeRobot Dataset Conversion: The ConvertToLeRobotMapper provides functionality to transform HDF5 datasets (from MimicGen/Isaac Lab) into the standardized LeRobot dataset format, including handling video metadata and data structuring.
• Simulator Dependencies and Documentation: A new [simulator] extra dependency group has been introduced in pyproject.toml for Isaac Sim, Ray, and USD-core. The docs/Operators.md file has been updated to include the new mapper operators and their descriptions.
• Comprehensive Unit Testing: Dedicated unit tests have been added for each of the newly introduced mappers, utilizing mocking to ensure their functionality and stability without requiring a full Isaac Sim environment during testing.

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Code Review

此PR为物理AI数据处理添加了四个新的mapper算子，集成了Isaac Sim，这是一个重要的功能增强。新的算子annotate_demos、generate_dataset、replay_randomized和convert_to_lerobot覆盖了从种子数据到可训练数据的完整流程。PR还包括了文档更新、新的依赖项和单元测试。代码结构总体良好，但存在一些关键问题需要解决，特别是关于安全（eval的使用）、依赖冲突和潜在的资源泄漏。此外，还有几个中等严重性的问题涉及代码风格和最佳实践，应予以修复以提高可维护性。