feat: add test tube pick task with custom assets and grasp annotations

- Add pick_test_tube task: USDC asset repackaging, grasp generation, task config
- Add tools: usdc_to_obj.py, repackage_test_tube.py, fix_test_tube_materials.py
- Add custom_task_guide.md: full Chinese documentation for creating custom tasks
- Add crawled InternDataEngine online docs (23 pages)
- Add grasp generation script (gen_tube_grasp.py) and pipeline config

docs_crawled/policy_training.md

@@ -0,0 +1,83 @@
+# Source: https://internrobotics.github.io/InternDataEngine-Docs/policy/training.html
+
+# Training [​](#training)
+
+This guide covers data format conversion and policy training for validating generated simulation data.
+
+## Part 1: LMDB to LeRobot Data Conversion [​](#part-1-lmdb-to-lerobot-data-conversion)
+
+The simulation data generated by InternDataEngine is stored in LMDB format. To use this data for policy training, you need to convert it to LeRobot format.
+
+### Step 1: Install LeRobot v2.1 [​](#step-1-install-lerobot-v2-1)
+
+We use LeRobot v2.1 format for data storage. Install the LeRobot 2.1 repo.
+
+### Step 2: Convert LMDB to LeRobot v2.1 [​](#step-2-convert-lmdb-to-lerobot-v2-1)
+
+Use the conversion scripts in ``[policy/lmdb2lerobotv21](https://github.com/InternRobotics/InternDataEngine/tree/master/policy/lmdb2lerobotv21)directory.
+
+We provide conversion scripts for different robot platforms:
+
+- **lmdb2lerobot_lift2_a1.py **( script ): Lift2 (ARX).
+- **lmdb2lerobot_split_aloha_a1.py **( script ): Split Aloha.
+- **lmdb2lerobot_genie1_a1.py **( script ): Genie1.
+- **lmdb2lerobot_franka_a1.py **( script ): Franka FR3.
+- **lmdb2lerobot_frankarobotiq_a1.py **( script ): Franka with Robotiq gripper.
+
+Example usage:
+bash
+```
+python lmdb2lerobot_lift2_a1.py \
+    --src_path ${src_path} \
+    --save_path ${save_path} \
+    --repo_id ${repo_id} \
+    --num-threads ${num_threads} \
+    --num_demos ${num_demos}
+```
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+3
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+
+**Parameters: **
+
+- **--src_path **( str ): Path to the source LMDB data directory.
+- **--save_path **( str ): Path to save the converted LeRobot dataset.
+- **--repo_id **( str ): Dataset repository identifier.
+- **--num-threads **( int ): Number of threads for parallel processing.
+- **--num_demos **( int ): Number of demonstrations to convert (optional).
+
+### Step 3: Convert to LeRobot v3.0 (Optional) [​](#step-3-convert-to-lerobot-v3-0-optional)
+
+If you need LeRobot v3.0 format for training, please install LeRobot 3.0. Then use the conversion script:
+bash
+```
+python convertv21_to_v30.py --input_path ${v21_path} --output_path ${v30_path}
+```
+1
+
+The conversion code is available at ``[policy/lmdb2lerobotv21/convertv21_to_v30.py](https://github.com/InternRobotics/InternDataEngine/tree/master/policy/lmdb2lerobotv21/convertv21_to_v30.py).
+
+## Part 2: Policy Training with π 0 [​](#part-2-policy-training-with-π0)
+
+As described in the [InternData-A1 paper](https://arxiv.org/pdf/2511.16651), we used multi-machine, multi-GPU JAX-based π 0 for data validation.
+
+We have implemented a JAX-based, multi-nodes, multi-GPU training pipeline that supports multi-dataset mixed training for π 0 .
+
+### Features [​](#features)
+
+- **Multi-machine, multi-GPU training **: Scale training across multiple nodes
+- **Multi-dataset mixed training **: Train on multiple datasets simultaneously
+- **JAX-based implementation **: High-performance training with JAX/Flax
+
+### Installation, Training, and Deployment [​](#installation-training-and-deployment)
+
+For detailed instructions on installation, training, and deployment, please refer to the [openpi-InternData-A1 README](https://github.com/InternRobotics/InternDataEngine/blob/master/policy/openpi-InternData-A1/README.md).
+
+## References [​](#references)
+
+- [LeRobot](https://github.com/huggingface/lerobot)- HuggingFace LeRobot
+- [InternData-A1 Paper](https://arxiv.org/pdf/2511.16651)- InternData-A1: A High-Fidelity Synthetic Data Generator for Robotic Manipulation
+- [openpi-InternData-A1](https://github.com/InternRobotics/InternDataEngine/blob/master/policy/openpi-InternData-A1/)- JAX-based π 0 training code