Add files using upload-large-folder tool

README.md

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+---
+license: other
+task_categories:
+- robotics
+- imitation-learning
+pretty_name: UP-VLA Precision Recovery 15K
+size_categories:
+- 10K<n<100K
+---
+
+# UP-VLA Precision Recovery 15K
+
+This research dataset contains 15,000 physically validated
+precision-recovery samples in 150 WebDataset shards.
+
+Each sample contains a wrist RGB image, metric depth, camera-frame XYZRGB point cloud,
+and synchronized dual action trajectories:
+
+1. relative end-effector SE(3) in the current tool frame;
+2. robot joint positions on the same timestamps.
+
+Perturbations are 5 mm to 5 cm and 5 to 30 degrees. Samples include deterministic
+randomization receipts for background textures, clutter, lighting, RGB photometrics,
+depth noise, and depth dropout.
+
+## Coverage
+
+- Unique object models: 111
+- Source families: {"rlbench": 5220, "robotwin_generic": 9780}
+- Task categories: {"edge_grasp": 1617, "insertion_alignment": 5200, "precision_grasp": 8183}
+- Domain-randomized samples: 15000
+- Background signatures: 5291
+- Scene signatures: 5293
+- Lighting signatures: 14659
+- Sensor signatures: 14573
+- All source experts successful: yes
+- All recovery tasks successful: yes
+
+## Format
+
+Use manifest.jsonl to map sample keys to shards/shard-*.tar. Each key has
+metadata.json, wrist_rgb.png, wrist_depth.npy, wrist_pointcloud.npy, and
+action_chunk.npz members.
+
+## License and source notice
+
+RoboTwin-derived samples use the MIT-licensed RoboTwin software and assets subject to
+their own provenance. RLBench-derived samples are for non-commercial internal or
+academic research under the RLBench licence from Imperial College London. Publications
+or presentations using those samples must identify RLBench as their source and comply
+with the upstream licence. This combined dataset is therefore distributed for
+non-commercial research use only.
+
+The dataset contains rendered observations and trajectories, not copies of simulator
+source code.

dataset_info.json

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