rerun-urdf

name	rerun-urdf
description	Drive the Rerun URDF API (rerun.urdf.UrdfTree) to ingest a URDF as a Transform3D layer on a robot recording. Read when logging a robot model, running forward kinematics from joint states, composing a fixed chain for sensor extrinsics, or when the transform tree will not connect from the data alone. Builds on rerun-chunk-processing (stream/lens mechanics) and rerun-data-model (entity paths, timeline, base-vs-layer).
user_invocable	true
allowed-tools	Read, Grep, Bash, WebFetch

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.

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, and several judgment calls below depend on that: urdf.joints() returns every UrdfJoint with name, joint_type, parent_link, child_link, axis, origin_xyz/origin_rpy, limits, and mimic (a UrdfMimic with driver joint, multiplier, offset, or None). get_joint_by_name, get_link_by_name, root_link(), and get_visual_geometry_paths(link) round it out. 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)          # rest-pose joint transforms too
    .drop(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.

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. The most common cause is a protobuf MCAP whose schema ships without a compiled descriptor; the fix (patching the descriptor into the MCAP before any reader touches it) is in rerun-mcap. Whatever the source, confirm the joint-state stream yields rows before debugging FK.

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:

import rerun as rr
from rerun.experimental 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.experimental 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_TOPIC, 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_TOPIC are the only data-specific pieces, and the mapping section above is what makes them correct.

Gotchas that cause real failures

1. Empty layer, no error: dead joint-state source (e.g. unpatched protobuf MCAP descriptor, see rerun-mcap) or wrong JOINT_TOPIC/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 mis-orders chunks: OBJECT_STORE optimization skipped.

Verify before relying on these

Verified against rerun-sdk 0.34.0a1. UrdfTree, JointTransformBatch, and the Transform3D.descriptor_* accessors are rerun.urdf / rerun.experimental surface; on upgrade re-check with python -c "from rerun.urdf import UrdfTree; help(UrdfTree)".

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)
• rerun-data-model (the mapping table this skill consumes)
• rerun-mcap (making MCAP joint-state topics readable)
• rerun-chunk-processing (lens/stream, write/optimize mechanics)