--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - roboteur - openarm - manipulation - sim2real configs: - config_name: default data_files: data/*/*.parquet --- # roboteur/r1_block_tower_fixed_view Simulated bimanual manipulation on **Roboteur R1** — an OpenArm v2 pair on a legged torso — rendered in SAPIEN with path tracing. Generated with [`r1-sim`](https://github.com/roboteur-research/r1-sim). ## What is in it | | | |---|---| | episodes | 300 | | frames | 225341 | | control / video rate | 30 Hz | | camera streams | 4 x 640x360 | | robot | `roboteur_r1_openarm2`, 19 state channels | Cameras: `head_left`, `head_right`, `wrist_left`, `wrist_right`. ### Features | feature | dtype | shape | units | |---|---|---|---| | `action` | float32 | `[19]` | rad (absolute joint positions; gripper = signed finger angle, 0 shut) | | `action.tcp_pose` | float32 | `[16]` | m (xyz) / unit quaternion (wxyz) / rad (gripper), robot base frame | | `observation.state` | float32 | `[19]` | rad (absolute joint positions; gripper = signed finger angle, 0 shut) | | `observation.velocity` | float32 | `[19]` | | | `observation.tcp_pose` | float32 | `[16]` | m (xyz) / unit quaternion (wxyz) / rad (gripper), robot base frame | | `observation.body` | float32 | `[4]` | | | `subtask_index` | int64 | `[1]` | | | `observation.images.head_left` | video | `[360, 640, 3]` | uint8 RGB | | `observation.images.head_right` | video | `[360, 640, 3]` | uint8 RGB | | `observation.images.wrist_left` | video | `[360, 640, 3]` | uint8 RGB | | `observation.images.wrist_right` | video | `[360, 640, 3]` | uint8 RGB | Channel names follow the [OpenArm dataset spec](https://github.com/enactic/openarm_dataset) (v0.4.0): `f"{component}_{joint}.pos"` with the **right arm first**, then the left. So `observation.state` is exactly the real recorder's 19-vector (openarm-teleop) — `state[:16]` the spec's 16-wide OpenArm block, `state[16:19]` the head pan/tilt/roll — byte-compatible with a real teleop recording, no slicing needed. The sim-only waist and leg joints ride in a separate `observation.body` feature that a real dataset simply does not have. `action` is the same 19 channels in the same order (arm targets plus the head hold command), so **state and action are index-comparable** — column *i* of one is the same physical joint as column *i* of the other. `observation.velocity` carries the measured joint velocities under `.vel` names; there is no effort feature because the sim does not measure torque, and a zero-filled placeholder would be worse than an absent one. Per-step object poses are **deliberately not exported**. They are privileged state, and on a task whose point is that the instruction picks the object, a policy handed exact poses can succeed without reading the instruction or the image. They remain in the source HDF5, and the scene ground truth is in `meta/r1_scenes.jsonl`. ## Units and conventions | quantity | unit | note | |---|---|---| | joint positions | **radians** | absolute, not deltas; matches the spec's own sample values | | gripper | **radians**, signed | `0.0` = jaws **shut**; the right finger opens toward `-0.785`, the left toward `+0.785` | | end-effector position | **metres** | `x y z`, in the **robot base frame** | | end-effector rotation | unit quaternion | `qw qx qy qz` — **w first** | | object poses | metres + `qw qx qy qz` | **world frame**, not the base frame | | timestamp | seconds | | | images | `uint8` RGB | 640x360 | ### Gripper polarity **The raw signed finger angle, matching the real recorder (openarm-teleop).** `0.0` is shut on both arms; the URDF mirrors the arms, so the right finger joint runs `0 → −0.7854` as it opens where the left runs `0 → +0.7854`. Nothing is folded to a magnitude: `|value|` is the openness, and the sign is the side. `meta/r1_frames.json` restates this per side, machine-readably. - The controller takes the gripper as a *normalised* command in `[−1, +1]` while it takes the arm joints as absolute radians. The exporter converts that command into the same signed radians the state reports, so `left_gripper.pos` means the same quantity in `action` as it does in `observation.state`. - Each gripper has two finger joints in the URDF and the second is a `` of the first (multiplier 1, offset 0) — the same number twice. Only one is logged. `|openness|` maps to a fingertip gap at the grasp point, measured off the collision meshes: | openness (rad) | 0.000 | 0.196 | 0.393 | 0.589 | 0.785 | |---|---|---|---|---|---| | jaw gap (mm) | 0 | 36 | 57 | 74 | 92 | ### Frames End-effector poses are **relative to the robot base**, not the world. The two coincide numerically throughout this dataset — every stance here parks the base at the world origin with identity rotation — so a reader that assumes world frame gets the right answer by luck. Two stances in the library do not, and `meta/r1_frames.json` records the frame of every Cartesian feature. The base pose that converts between them is in `meta/r1_scenes.jsonl` per episode, as `task_info.robot_pose`. ## Variation Every episode randomises the scene and the robot's posture; the recipe for each one is in `meta/r1_scenes.jsonl`, keyed by episode index. The run-level settings those draws came from — shader and ray-tracing settings, lighting/dressing mode, clutter and posture ranges — are in `meta/r1_generation.json`. - **distractors** — 3–10 objects drawn from a 415-instance library, placed clear of the task and of each other - **appearance** — floor, wall and table textures drawn independently, plus lighting jitter - **posture** — six initial arm-posture families with elbow swivel, sampled per arm; the idle arm stays where it starts - **height** — the robot stands at one of five measured leg configurations - **roles** — which objects play the task's roles varies per episode ## Success 300 of 300 episodes end with the task's success check satisfied. The generator retries a seed whose scripted expert fails and keeps only successes, so this is a clean-demonstration corpus, not a measure of the expert's success rate. Where the expert had to fall back from a Cartesian path to a joint-space one, the step is noted per episode in `expert_failures` (57 across the corpus). These are recovered episodes, not failed ones.