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Datasets:
flex-pi
/
soft_bag_zipping

Tasks:
Robotics
Modalities:
Tabular
Time-series
Video
Formats:
parquet
Size:
1M - 10M
Tags:
LeRobot
robotics
manipulation
yam
depth
bimanual
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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  1. README.md +135 -0
  2. videos/chunk-000/observation.images.cam_right_wrist/episode_000000.mp4 +3 -0
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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - robotics
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+ tags:
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+ - LeRobot
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+ - robotics
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+ - manipulation
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+ - yam
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+ - depth
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+ - bimanual
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+ configs:
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+ - config_name: default
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+ data_files: data/*/*.parquet
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+ ---
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+
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+ # soft_bag_zipping
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+
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+ Deformable-object manipulation: unzip a soft bag, insert pens one at a time, then zip the bag closed.
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+
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+ Real-robot bimanual manipulation data collected on a **YAM** arm pair, released as part
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+ of the Flex-π project. Stored in **LeRobot v2.1** format with **synchronized RGB and
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+ metric depth** from three cameras.
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+
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+ ## At a glance
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+
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+ | | |
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+ |---|---|
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+ | Episodes | 534 |
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+ | Frames | 1,047,427 |
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+ | Duration | ~9.7 h @ 30 fps |
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+ | Tasks | 1 |
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+ | Robot | `yam` (bimanual) |
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+ | Cameras | `cam_high`, `cam_left_wrist`, `cam_right_wrist` |
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+ | RGB | 640×360, H.264 `.mp4` |
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+ | Depth | 640×360, FFV1 `.mkv`, uint16 **millimetres** |
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+ | State / action | 32-D / 32-D |
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+ | LeRobot version | `v2.1` |
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+
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+ ## Tasks
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+
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+ 0. Unzip the bag, pick up the pens from the table one at a time and place them inside, then zip the bag closed.
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+
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+ ## Layout
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+
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+ ```
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+ meta/
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+ info.json # feature schema, totals, chunking
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+ tasks.jsonl # task_index -> natural-language instruction
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+ episodes.jsonl # per-episode length + task
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+ episodes_stats.jsonl # per-episode min/max/mean/std for state & action
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+ camera_intrinsics.json # per-camera pinhole K at stored resolution
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+ data/chunk-{NNN}/episode_{NNNNNN}.parquet
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+ videos/chunk-{NNN}/observation.images.{cam}/episode_{NNNNNN}.mp4 # RGB
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+ videos/chunk-{NNN}/observation.depth_ffv1.{cam}/episode_{NNNNNN}.mkv # depth
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+ ```
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+
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+ Episodes are indexed `0 .. 533`, chunked at
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+ 1000 episodes (1 chunk).
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+ The parquet `index` column is a **global** frame counter running
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+ `0 .. 1,047,426` across the whole dataset.
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+
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+ ## Camera intrinsics
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+
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+ Pinhole `K` at the stored 640×360 resolution, averaged over episodes:
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+
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+ | camera | fx | fy | cx | cy |
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+ |---|---|---|---|---|
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+ | `cam_high` | 262.27 | 262.12 | 320.51 | 183.18 |
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+ | `cam_left_wrist` | 365.72 | 365.50 | 318.08 | 182.79 |
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+ | `cam_right_wrist` | 366.90 | 366.68 | 324.67 | 171.96 |
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+
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+ ## Reading the depth
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+
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+ > **The depth streams are an extension to stock LeRobot.** They are declared with
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+ > `dtype: "depth_video"` (not `"video"`) in `meta/info.json` precisely so that the
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+ > stock `LeRobotDataset` loader skips them — you get a working RGB dataset out of the
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+ > box, and depth needs the decoder below.
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+
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+ Each depth frame is a single-channel **uint16, millimetre** map, FFV1-encoded in
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+ `gray16le` inside a Matroska container. `0` means no return. To decode a frame:
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+
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+ ```python
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+ import av, numpy as np
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+
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+ with av.open("videos/chunk-000/observation.depth_ffv1.cam_high/episode_000000.mkv") as c:
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+ for frame in c.decode(video=0):
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+ depth_mm = frame.to_ndarray(format="gray16le").astype(np.uint16) # (H, W)
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+ depth_m = depth_mm.astype(np.float32) / 1000.0
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+ ```
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+
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+ FFV1 is lossless, so the decoded values are bit-exact with what the sensor reported.
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+ Do **not** transcode these to a lossy codec.
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+
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+ ## State and action layout
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+
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+ `observation.state` and `action` are both 32-D. The vector is grouped **by field**, not
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+ by arm:
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+
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+ | index | contents |
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+ |---|---|
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+ | `0:3` | `left_pos_{x,y,z}` — left end-effector position |
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+ | `3:9` | `left_rot6d_{0..5}` — left end-effector rotation, 6-D representation |
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+ | `9:12` | `right_pos_{x,y,z}` |
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+ | `12:18` | `right_rot6d_{0..5}` |
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+ | `18:20` | `left_gripper`, `right_gripper` |
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+ | `20:26` | `left_joint_{0..5}` |
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+ | `26:32` | `right_joint_{0..5}` |
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+
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+ The authoritative per-dimension names are in `meta/info.json` under
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+ `features.observation.state.names`. The 6-D rotation is the **first two rows** of the
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+ 3×3 rotation matrix, row-major flattened (Zhou et al., *On the Continuity of Rotation
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+ Representations*); recover `R` by Gram–Schmidt on those two rows and their cross
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+ product.
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+
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+ ## Loading
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+
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+ RGB only, with stock LeRobot:
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+
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+ ```python
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+ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
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+ ds = LeRobotDataset("flex-pi/soft_bag_zipping")
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+ ```
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+
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+ RGB + depth: use the `RobotPerCamDepthVideoDataset` loader from the Flex-π / FastWAM
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+ codebase, which reads the `depth_encoder` / `depth_ext` fields in `meta/info.json`.
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+
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+ ## Provenance
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+
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+ Built with `fastwam-eef32-action26-openpi-aligned` from source recordings
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+ `put_pen_into_green_bag_V2`, `put_pen_into_grey_bag_v2`, `put_pen_into_pink_bag_V2`, `put_pen_into_grey_and_purple_bag_V2`, `put_pen_into_orange_and_green_bag_V2`.
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+
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+ ## Citation
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+
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+ If you use this dataset, please cite the Flex-π project.
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