rerun-urdf

Rerun URDF ingestion

A URDF gives you a robot's geometry and its kinematic tree. Ingesting it means two API calls on one rerun.urdf.UrdfTree: stream the static model, then drive it with forward kinematics from joint states. The transforms it produces are derived, so they are a layer, never base (the URDF + joints (computed) row of the rerun-data-model table).

This skill is the UrdfTree API and the two judgment calls it cannot make for you: how your joint values map to URDF joints, and how the disconnected frames in the scene connect to one root. The stream/lens plumbing (LazyChunkStream, DeriveLens, Selector, writing and optimizing RRDs) is in rerun-chunk-processing; reach for it, do not re-derive it. Nothing below is tied to a data format: where your joint names, joint values, and calibration come from is yours to wire in.

Getting the URDF

Datasets rarely ship one, and the robot's own repo is not always the fastest route. In order:

• robot_descriptions.py — pip install robot_descriptions, then from robot_descriptions import ur5e_description; ur5e_description.URDF_PATH. It downloads and caches the upstream description and hands you a plain path with the meshes already next to it. Check its index first; it covers most of the table below.
• The vendor repo, when the description is newer than the package's pin or is not in it.
• MuJoCo Menagerie — MJCF, not URDF, so it needs conversion. Worth it only when nothing else exists.

Robot	Source	Notes
SO-100 / SO-101 (LeRobot)	TheRobotStudio/SO-ARM100	Simulation/SO100/so100.urdf, Simulation/SO101/so101_new_calib.urdf; plain URDF, relative mesh paths
ALOHA (ViperX 300, WidowX)	Interbotix/interbotix_ros_manipulators	xacro
Franka Panda / FR3	frankaemika/franka_description	xacro
Universal Robots UR3 to UR20	UniversalRobots/Universal_Robots_ROS2_Description	xacro, arm parameters in YAML
Kinova Gen3	Kinovarobotics/ros2_kortex	xacro
UFactory xArm / Lite 6	xArm-Developer/xarm_ros2	xacro
Unitree Z1, G1, H1, Go2	unitreerobotics/unitree_ros	plain URDF per robot

Two things bite before FK ever runs:

• xacro is not URDF. First run xacro arm.urdf.xacro > arm.urdf (pip install xacro), passing whatever arguments the file requires (gripper variant, arm size). A xacro file handed to UrdfTree.from_file_path fails to parse.
• package:// mesh URIs. The importer resolves them by scanning ROS_PACKAGE_PATH (ROS 1) and AMENT_PREFIX_PATH (ROS 2) for the package, and otherwise treats the rest of the URI as relative to the URDF's own directory. Outside a sourced ROS workspace, either export one of those variables or lay the meshes out relative to the URDF. Meshes that fail to resolve give you a kinematically correct but invisible robot.

The API

from rerun.urdf import UrdfTree

urdf = UrdfTree.from_file_path(
    urdf_path,
    entity_path_prefix="robot",  # links log under /robot/<link>
    frame_prefix="",  # prepended to every frame name
    static_transform_entity_path="robot/tf_static",
)

• entity_path_prefix namespaces the entity tree. One per robot instance.
• frame_prefix namespaces the frame names (base_link -> arm_base_link). Two robots in one recording need different prefixes or their roots collide and transforms cross-wire. Leave it empty for a single robot.
• static_transform_entity_path is where the URDF's fixed-joint transforms log (defaults to /tf_static).

The tree is also introspectable (full surface in help(UrdfTree)) For one-off, non-stream use there are joint.compute_transform(value), joint.compute_transform_columns(values) (feeds rr.send_columns), and urdf.log_urdf_to_recording() to log the whole model through the classic logging API (the animated_urdf example, https://github.com/rerun-io/rerun/tree/main/examples/python/animated_urdf, is that style).

For pipelines, a UrdfTree does two things.

1. Stream the static model. Emits the visual meshes (Asset3D) and the fixed-joint transforms as chunks. This is the whole "log the URDF" step:

model = (
    urdf.stream(include_joint_transforms=True).drop(  # rest-pose joint transforms too
        content="/robot/**/collision_geometries/**"
    )  # unless you need collision meshes
)

Recolor a robot's meshes with a MutateLens on Asset3D:albedo_factor (see rerun-chunk-processing).

2. Solve forward kinematics. Given joint names and the matching joint values, it returns one rerun.urdf.JointTransformBatch per input row. Each batch is a list of per-joint entries with parent_frame, child_frame, translation, and quaternion:

batches = urdf.compute_joint_transform_batches(names, values, clamp=False)
# names, values: pyarrow arrays, one list per timestamp (names aligned to values)
# clamp=True clamps out-of-limit values and warns, useful while debugging units

You will almost always run this inside a stream so it stays columnar and lazy, via the two-lens pattern: derive the batch, then scatter=True it into Transform3D.descriptor_translation/quaternion/parent_frame/child_frame. The full shape is the "Minimal shape" section below; the robot_data_preprocessing example (References) is a complete working instance. The only URDF-specific part is the compute_joint_transform_batches call inside the first lens; everything else is generic stream mechanics (rerun-chunk-processing).

Merge the model stream and the FK stream, collect(optimize=OBJECT_STORE), and write_rrd(..., recording_id=<segment_id>). The recording_id must equal the base segment id or the layer never attaches; application_id is discarded on registration.

Mapping joint state to URDF joints (you supply this; the data will not)

compute_joint_transform_batches is only as right as the names/values you hand it, and the mapping is not in the URDF. Three things go wrong silently:

• Order. Build an explicit names array aligned to the values you read. Never assume your message's field order matches the URDF's <joint> order.
• Count. The URDF's non-fixed joint count rarely equals your reported value count. A gripper sent as one value is often two prismatic joints in the URDF; a mimic joint may be omitted from the message. Reconcile explicitly, and use the API to do it: iterate urdf.joints(), partition by joint_type and mimic. A joint with mimic set derives its value from the driver joint as driver * multiplier + offset; feed it that, not a message field. Confirm the count against urdf.joints(), not the message length.
• Units. URDF joints are radians (revolute) and meters (prismatic). Convert if your source differs.

Get any of these wrong and FK runs and writes a confident, wrong pose.

Verify joint mapping and limits

Joint values in the source data aren't guaranteed to be within URDF joint limits. This can happen with any format (MCAP, Parquet, etc). It is therefore useful to check whether joint values in the data match the URDF model's limits. Out-of-range values can indicate issues like wrong indexing or model mismatch and should be clarified before calculating the forward kinematics.

To get limits of a URDF model:

limits = {joint.name: (joint.limit_lower, joint.limit_upper) for joint in urdf.joints() if joint.joint_type != "fixed"}
# then compare each column's min/max against its joint's range

scripts/check_joint_mapping.py is that check worked out for one source shape — a LeRobot-style parquet column of per-frame joint values. Read it as the example to copy and adapt for your own format, and run it directly if your data is already a LeRobot-style parquet file:

uv run scripts/check_joint_mapping.py <urdf> <values.parquet> --column observation.state --degrees

Important: this is a validation meant to be done before full processing — don't run this for every file in a larger dataset. Select a single representative file or a small subset for this verification.

For each joint it tries the usual candidate corrections (identity, negate, % 360, ±90°/±180° offsets, and their combinations) and reports which ones land inside that joint's limits. It reports the fit, not the answer: a joint with two surviving candidates is ambiguous, and no amount of range-checking will separate them. Validate ambiguous mappings against known poses or geometry for the dataset.

Read the failures by shape. A whole column outside its range means a wrong sign, unit, or offset — a bug, and the pose will be confidently wrong. A few frames a few degrees past a limit is calibration slack — expected, and clamp=True is the fix, not a workaround. clamp=True warns once per offending row, so run it while working out the mapping: a flood of warnings is a mapping smell, and a thin trickle at the extremes of one joint is a healthy recording.

Make the joint states readable first

Where the joint values come from is not this skill's problem, but a dead joint source produces an empty FK layer with no error: a source path that matched nothing, a decoder that yielded zero rows, or a reader that dropped the message silently. Whatever the source, confirm the joint-state stream yields rows before debugging FK. The importer skill for your source format covers its own empty-stream failure modes.

How transforms compose (reason about this before logging anything)

Rerun resolves a pose by chaining transforms from a frame up to a root. There are two ways an edge in that chain gets defined, and a URDF ingest mixes both:

• By entity-path hierarchy. A Transform3D on /a/b with no frame names is the transform of /a/b relative to its parent path /a. Composition follows the entity tree.
• By explicit frame graph. A Transform3D that carries parent_frame and child_frame defines an edge between two named frames, independent of where in the entity tree it is logged. URDF FK uses this: every joint transform names its parent and child link frames.

So a URDF ingest is a graph of named frames. An edge exists only if some Transform3D names that exact parent_frame -> child_frame pair. A frame with no incoming edge is a root. The viewer renders every root at the world origin, which is why two unconnected robots silently overlap instead of erroring.

Resolving the transform forest (the part the data cannot always give you)

A URDF is one tree rooted at its base link. FK and fixed joints supply every edge inside that tree. A real scene is a forest of roots the URDF never connects: a world or scene frame, each robot's base, every camera or sensor frame. The edges that join those roots come from calibration (extrinsics in a sidecar, a TF static publisher, a hand-eye result), not from the URDF, and some are simply absent. A single connected tree is not always solvable. Resolve it deliberately:

1. Enumerate every frame. Iterate urdf.joints() and collect the parent_link/child_link pairs (the in-tree edges, frame-prefixed); urdf.root_link() is that URDF's root. Add every sensor/world frame the scene needs (from the rerun-data-model table). Decide the one intended root.
2. Classify each edge by source. In-URDF edges (FK joints, fixed joints) come from the URDF plus joint values. Inter-root edges (root to each robot base, root to each fixed sensor, an arm link to a wrist-mounted camera) come from calibration and you must log them yourself.
3. Compose fixed chains from the URDF when you need the transform between two links joined only by fixed joints (a camera bracket, a tool mount): walk parent links across fixed joints via urdf.joints(), turning each joint's origin_xyz/origin_rpy into a homogeneous matrix and multiplying along the chain. If the walk cannot reach the target link, stop and say so ("no fixed chain from A to B; stuck at C"). A broken chain is a wrong pose, not a missing one.
4. Build the edge set and find the roots. Collect every parent_frame -> child_frame pair you will log (URDF + calibration). Walk parents from each frame; any frame that does not reach the intended root is an unconnected root and names a missing edge. This is a pure graph check you can run before writing the RRD.
5. Resolve every missing edge, or fail loudly. For each one:
   • If calibration supplies the transform, log it (next section).
   • If the data does not, the tree is unsolvable. Do not leave the frame disconnected (it collapses onto the origin and reads as one merged scene). Either abort and name the missing edge, or log identity and emit a loud warning naming the assumed edge. State which you did.
6. Match frame names across sources. FK-derived parent_frame/child_frame must equal the names urdf.stream() emits for the static geometry, and your calibration edges must use those same names, or links float off the mesh. The frame_prefix is what keeps them identical; reuse it everywhere.

A correct ingest has exactly one root, and a path from every frame to it.

Logging a connection correctly

A connecting edge is a static Transform3D carrying the bridging frame names. Static (no time index) so it holds for the whole recording; the frame names, not the entity path, are what create the graph edge. This Chunk.from_columns is the rare sidecar exception — a calibration transform no reader or FK lens can produce; do not generalize it to transforms a reader emits (a frame_transforms topic → Transform3D) or that FK derives:

import rerun as rr
from rerun.chunk import Chunk, LazyChunkStream

# world -> this robot's base, from your calibration (translation + xyzw quaternion)
edge = Chunk.from_columns(
    "/world/robot_base",  # any sensible bridging entity path
    indexes=[],  # no index == static
    columns=rr.Transform3D.columns(
        translation=[translation],
        quaternion=[quaternion_xyzw],
        parent_frame=["world"],  # must match the root frame name
        child_frame=["arm_base_link"],  # must match the URDF root frame (prefixed)
    ),
)
edges = LazyChunkStream.from_iter([edge])  # merge alongside model + FK streams

Log a fixed-chain result (step 3) the same way, with parent_frame and child_frame set to the two link frames the chain spans. Merge all edge chunks into the same recording as the model and FK streams so they share the graph.

Minimal shape (one robot, generic joint source)

import rerun as rr
from rerun.chunk import DeriveLens, LazyChunkStream, OptimizationProfile, Selector
from rerun.urdf import UrdfTree

urdf = UrdfTree.from_file_path(urdf_path, entity_path_prefix="robot", static_transform_entity_path="robot/tf_static")
model = urdf.stream(include_joint_transforms=True).drop(content="/robot/**/collision_geometries/**")

joints = source_joint_state_stream()  # your reader; one message column of names+values
fk = (
    joints
    .lenses(
        DeriveLens(JOINT_MSG_COMPONENT, output_entity="/tmp/batches").to_component(
            "rerun.urdf.JointTransformBatch",
            Selector(".").pipe(lambda msgs: urdf.compute_joint_transform_batches(read_names(msgs), read_values(msgs))),
        ),
        content=JOINT_SOURCE_PATH,
        output_mode="forward_all",
    )
    .lenses(
        DeriveLens("rerun.urdf.JointTransformBatch", output_entity="/robot/transforms", scatter=True)
        .to_component(rr.Transform3D.descriptor_translation(), Selector("[].translation"))
        .to_component(rr.Transform3D.descriptor_quaternion(), Selector("[].quaternion"))
        .to_component(rr.Transform3D.descriptor_parent_frame(), Selector("[].parent_frame"))
        .to_component(rr.Transform3D.descriptor_child_frame(), Selector("[].child_frame")),
        content="/tmp/batches",
        output_mode="drop_unmatched",
    )
    .filter(content="/robot/transforms")
)

LazyChunkStream.merge(model, fk).collect(optimize=OptimizationProfile.OBJECT_STORE).write_rrd(
    out_path,
    application_id="urdf",
    recording_id=segment_id,
)

read_names, read_values, JOINT_MSG_COMPONENT, and JOINT_SOURCE_PATH are the only data-specific pieces, and the mapping section above is what makes them correct.

One URDF for a whole dataset (assets)

urdf.stream() emits only static chunks — meshes and fixed-joint transforms, with no time index. The model does not belong in a per-episode layer, where every segment pays for the same meshes. Register it once as a dataset asset:

urdf.stream().collect(optimize=OptimizationProfile.OBJECT_STORE).write_rrd(
    model_rrd, application_id="urdf", recording_id="so100_model"
)
asset_id = dataset.register_asset("s3://bucket/so100_model.rrd")  # uri must be readable by the server

The split is along static vs temporal: the model is an asset, and the FK transforms stay a per-segment layer keyed by recording_id. Keeping include_joint_transforms=True in the asset costs nothing and gives the rest pose for segments or time ranges the FK layer does not cover.

• The viewer loads a dataset's assets alongside any segment it opens, and caches them across segments.
• dataset.segment_store(segment_id) includes assets; pass include_assets=False to skip the extra manifest request per asset.
• Dataframe queries never see assets — they stay on the dataset's own segments.

Without a catalog, the same split still pays: write the model RRD once and open it next to whichever episode you are looking at, rather than copying it into each one.

Gotchas that cause real failures

1. Empty layer, no error: dead joint-state source (decoded to zero rows; see the importer skill for your format) or wrong JOINT_SOURCE_PATH/component name.
2. Confident wrong pose: joint count, order, or units wrong.
3. Layer writes but never attaches: recording_id != segment_id.
4. Frames collide: two robots sharing a frame_prefix.
5. Scene looks merged at the origin: unconnected roots logged as identity without a calibration edge.
6. Catalog ingest rejects or misorders chunks: OBJECT_STORE optimization skipped.
7. Layer opened in a running viewer never merges: opening an .rrd by path registers it as its own dataset and replaces its application_id, so it lands beside the segment instead of on it. To eyeball a layer locally, either merge model + FK + base into one RRD, or send both through a RecordingStream(application_id, recording_id=segment_id) pointed at the viewer with rec.connect_grpc(url="rerun+http://127.0.0.1:9876/proxy"), which keeps the IDs and merges the stores.
8. Robot invisible but transforms correct: meshes failed to resolve (see "Getting the URDF").
9. No 3D view after adding a URDF to an existing recording: the auto-layout does not necessarily rebuild for the new entities. Send an explicit blueprint with rrb.Spatial3DView(origin="/robot") (see rerun-blueprint).

References

• https://github.com/rerun-io/rerun/tree/main/examples/python/robot_data_preprocessing (FK two-lens pattern, two robots + scene URDFs, prefixes, recoloring, calibration offsets)
• https://github.com/rerun-io/rerun/tree/main/examples/python/animated_urdf (classic logging API: log_urdf_to_recording, per-joint compute_transform)
• Catalog object model: assets (register_asset, the limits, and how assets are read back)
• rerun-data-model (the mapping table this skill consumes)
• the importer skill for your joint-state source format (making the source readable)
• rerun-chunk-processing (lens/stream, write/optimize mechanics)