azorematter/dice_white_pnp_act_joint7

This dataset was created using LeRobot.

Dataset Description

Real-robot pick-and-place on a FANUC CRX-5iA collaborative arm: a dice is picked up and placed on an empty white block, from four camera views. Recorded by a scripted visual-servo pipeline (not teleoperation), so the demonstrations are consistent and the placements are verified — an episode is only saved if a post-hoc survey confirms the dice actually seated on the block it was aimed at.

Prepared for training task-conditioned ACT: the action has been narrowed to absolute joint targets, and a per-task one-hot rides in observation.environment_state.

• Episodes: 118 · Frames: 83,472 · FPS: 30
• Robot: fanuc_crx5ia · LeRobot format: v3.0
• Cameras: 4 × 640×480 (4:3)
• Episode length: 205 – 865 frames (median 727, ≈24 s)

The contract

Read this before training. Several channels are not what a reader would guess.

action — 7 floats, ABSOLUTE joint targets

['J1', 'J2', 'J3', 'J4', 'J5', 'J6', 'gripper']

J1–J6 are degrees; gripper is binary. The action at frame t is the next frame's state — absolute positions, not deltas.

Joint space rather than Cartesian pose is deliberate. The W Euler channel sits 11.2° from the ±180° wrap (measured range −168.77…−154.41) where a value jumps 360° and an L1 loss cannot recover, while every joint sits mid-range with 80°+ of headroom. A joint vector is also one arm configuration where a Cartesian pose is many, so commanding pose would leave the elbow/wrist branch to the controller. And it matches ACT's origin — ALOHA commands joint positions.

observation.state — 13 floats

['J1', 'J2', 'J3', 'J4', 'J5', 'J6', 'x', 'y', 'z', 'W', 'P', 'R', 'gripper']

Wider than the action on purpose: x/y/z (mm) and W/P/R (degrees) are the TCP pose, free as input and informative, but harmful as prediction targets for the reason above.

⚠️ gripper: 1.0 means OPEN

Both in observation.state[12] and in the action's last channel. This is the recorder's convention and it is the opposite of the more common 1.0-means-closed. Feed this bit to a policy that assumes the other convention and it reads "open" as "already holding the part". It is the commanded latch, captured before the gripper macro fires, so the command correctly precedes the visible finger motion.

observation.environment_state — task one-hot

• 0 — dice_to_block — "Pick up the dice and place it on the empty white block."
• 1 — dice_from_board — "Pick up the dice from the board and place it on the empty white block."

This is the per-task embedding input. LeRobot's ACT projects observation.environment_state with a Linear and appends it to the encoder's 1-D tokens — and a Linear applied to a one-hot is an embedding lookup, so no fork of LeRobot is needed. Normalize it as IDENTITY; standardising a one-hot by column statistics stops it being a one-hot.

The width is frozen into trained weights. Adding a task changes it and invalidates the checkpoint.

Known caveats

• Episodes 0–79 have reconstructed states. The robot's telemetry rides a cache that freezes while a motion command holds the socket, so those episodes originally stepped in one frame per move. They were densified by linear interpolation between observed boundary poses — legitimate here because every move is a straight-line Cartesian move, but validated against ground truth it carries ≈2 mm mean / 8.7 mm p95 position error at grasp depth. Episodes 80–117 are natively dense and are the cleaner subset. Consider validating on 80–117, or weighting them higher.
• Task balance is uneven: 2 tasks, but the board-recovery task has only 6 episodes against 112 for the main task.
• Video is 4:3 as recorded. No cropping or letterboxing has been applied.
• Images are real throughout; only the state channel was reconstructed.

Training

python -m lerobot.scripts.lerobot_train \
    --dataset.repo_id=azorematter/dice_white_pnp_act_joint7 \
    --policy.type=act \
    --policy.chunk_size=40 \
    --policy.n_action_steps=40 \
    --batch_size=8 \
    --steps=100000

chunk_size is the parameter most worth tuning. 40 steps ≈ 1.33 s of motion at 30 fps — chosen to be about a second (per ACT's own tuning guidance) while still outlasting the ~1.15 s command round trip on this cell, so the arm never stalls waiting for the next chunk. Keep n_action_steps == chunk_size so each chunk is executed fully, and leave temporal ensembling off — it forces n_action_steps=1, which at this cell's command rate is ~14 minutes per episode.

Scale batch_size to your GPU and the learning rate with it (batch 64 ↔ 5e-5, batch 8 ↔ 1e-5). Four 480×640 views produce ~1200 encoder tokens, so batch 64 will not fit on a single consumer card.

Dataset Structure

meta/info.json:

{
    "codebase_version": "v3.0",
    "fps": 30,
    "features": {
        "observation.state": {
            "dtype": "float32",
            "shape": [
                13
            ],
            "names": [
                "J1",
                "J2",
                "J3",
                "J4",
                "J5",
                "J6",
                "x",
                "y",
                "z",
                "W",
                "P",
                "R",
                "gripper"
            ]
        },
        "action": {
            "dtype": "float32",
            "shape": [
                7
            ],
            "names": [
                "J1",
                "J2",
                "J3",
                "J4",
                "J5",
                "J6",
                "gripper"
            ]
        },
        "observation.images.gripper": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false,
                "video.g": 2,
                "video.crf": 30,
                "video.preset": 12,
                "video.fast_decode": 0,
                "video.video_backend": "pyav",
                "video.extra_options": {},
                "is_depth_map": false
            }
        },
        "observation.images.cam0": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false,
                "video.g": 2,
                "video.crf": 30,
                "video.preset": 12,
                "video.fast_decode": 0,
                "video.video_backend": "pyav",
                "video.extra_options": {},
                "is_depth_map": false
            }
        },
        "observation.images.cam1": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false,
                "video.g": 2,
                "video.crf": 30,
                "video.preset": 12,
                "video.fast_decode": 0,
                "video.video_backend": "pyav",
                "video.extra_options": {},
                "is_depth_map": false
            }
        },
        "observation.images.cam2": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false,
                "video.g": 2,
                "video.crf": 30,
                "video.preset": 12,
                "video.fast_decode": 0,
                "video.video_backend": "pyav",
                "video.extra_options": {},
                "is_depth_map": false
            }
        },
        "timestamp": {
            "dtype": "float32",
            "shape": [
                1
            ],
            "names": null
        },
        "frame_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "episode_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "task_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "observation.environment_state": {
            "dtype": "float32",
            "shape": [
                2
            ],
            "names": [
                "dice_to_block",
                "dice_from_board"
            ]
        }
    },
    "total_episodes": 118,
    "total_frames": 83472,
    "total_tasks": 2,
    "chunks_size": 1000,
    "data_files_size_in_mb": 100,
    "video_files_size_in_mb": 200,
    "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
    "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
    "robot_type": "fanuc_crx5ia",
    "splits": {
        "train": "0:118"
    }
}