Randomizing joint velocity limits

75 · Domain randomization

Five rotors spun up by the same small constant torque, each with a sampled joint_velocity_limit. Steel enforces velocity limits as rows only when the solver is constructed with enable_joint_velocity_limits=True; the limit values are read every step. With the rows on, each rotor levels off at its own limit; with the default solver they all keep accelerating.

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What to look at

• enable_joint_velocity_limits=True: each rotor plateaus at its sampled limit.
• Default: the limits are ignored.

Key parameter

SolverSteel(model, enable_joint_velocity_limits=True)

One row per limited DOF; the rows count against the articulation row capacity.

Try changing

• Lower a limit mid-run and watch the rotor brake to it.
• Drive the rotors with a velocity target instead of a torque.

Concept

When Steel sees a randomized value

What it is

The rows that use this input exist only if the solver was constructed with the feature enabled; the values themselves are read every step. Randomize the values, not whether the feature is on.

In this scene

Default: the limits are ignored.

Considerations

Cost

Call only, default solver, six-link chain and four boxes per world, 1024 worlds (1.12 ms per collide + step): JOINT_DOF_FORCE_PROPERTIES 25 µs (7 µs captured). Flags measured one at a time; combined calls and feature validation can cost more. Body-inertial and joint-DOF notifications also request a mass-matrix refresh.

Graph capture

These flags can be captured in a reset graph. Kinematic-flag changes, sleeping, and shape flags with sparse factors plus restitution run eagerly; captured calls skip some value checks.

Interactions

Turning the feature on later needs a new solver.

Use it when

Motors with uncertain top speed; build with enable_joint_velocity_limits=True.

The script

cd tutorials && python dr_velocity_limits.py prints each variant's final metric; --record out.json also writes the replay. Every tutorial imports replay.py from the same folder.

dr_velocity_limits.py

tutorials/dr_velocity_limits.py

# SPDX-FileCopyrightText: Copyright (c) 2026 Skild AI and the Steel contributors
# SPDX-License-Identifier: Apache-2.0

"""Velocity-limit randomization: per-world joint_velocity_limit, enabled at construction."""

import newton
import numpy as np
import warp as wp
from newton.solvers import SolverSteel

from replay import Recorder

DT = 1.0 / 240.0
SUBSTEPS = 8
WORLDS = 5
TORQUE = 0.05  # N m, constant on every rotor


def tf(x, y, z):
    return wp.transform((x, y, z), wp.quat_identity())


def build():
    rotor = newton.ModelBuilder()
    hub = rotor.add_link(xform=tf(0.0, 0.0, 0.5))
    rotor.add_shape_cylinder(
        hub, radius=0.12, half_height=0.02, cfg=newton.ModelBuilder.ShapeConfig(density=500.0)
    )
    rotor.add_shape_box(hub, xform=tf(0.1, 0.0, 0.03), hx=0.03, hy=0.015, hz=0.01)  # a marker
    j = rotor.add_joint_revolute(-1, hub, axis=newton.Axis.Z, parent_xform=tf(0.0, 0.0, 0.5))
    rotor.add_articulation([j])
    builder = newton.ModelBuilder()
    builder.replicate(rotor, WORLDS, spacing=(0.0, 0.35, 0.0))
    return builder.finalize()


def simulate(rec, label, limit, enable):
    model = build()
    # the velocity-limit rows exist only if they are enabled here
    solver = SolverSteel(model, enable_joint_velocity_limits=enable)
    model.joint_velocity_limit.assign(limit.astype(np.float32))  # rad/s, one DOF per world
    solver.notify_model_changed(newton.ModelFlags.JOINT_DOF_FORCE_PROPERTIES)
    state_0, state_1, control = model.state(), model.state(), model.control()
    control.joint_f.fill_(TORQUE)

    run = rec.variant(label, model)
    for _ in range(rec.frames(3.0)):
        omega = state_0.joint_qd.numpy()
        run.frame(state_0, **{f"limit {v:.1f} rad/s": w for v, w in zip(limit, omega)})
        for _ in range(SUBSTEPS):
            solver.step(state_0, state_1, control, None, DT)
            state_0, state_1 = state_1, state_0
    run.check(solver)


if __name__ == "__main__":
    rng = np.random.default_rng(14)
    limit = np.sort(rng.uniform(2.0, 12.0, WORLDS))
    rec = Recorder()
    simulate(rec, "enable_joint_velocity_limits=True", limit, True)
    simulate(rec, "velocity limits off (default): ignored", limit, False)
    rec.save()

replay.py

tutorials/replay.py

# SPDX-FileCopyrightText: Copyright (c) 2026 Skild AI and the Steel contributors
# SPDX-License-Identifier: Apache-2.0

"""Record Steel tutorial runs as replay JSON for the tutorials page, or print a summary.

Every tutorial imports this module. ``python NN_name.py`` runs the variants and prints
the final metric of each; ``--record out.json`` also writes body poses, shapes and metric
series that the page replays in 3D.
"""

import argparse
import base64
import json
import os
import subprocess
import sys
import time

import newton
import numpy as np
import warp as wp

from source_hash import source_sha256

_RENDERED_TYPES = {
    newton.GeoType.PLANE,
    newton.GeoType.SPHERE,
    newton.GeoType.CAPSULE,
    newton.GeoType.ELLIPSOID,
    newton.GeoType.CYLINDER,
    newton.GeoType.BOX,
    newton.GeoType.MESH,
    newton.GeoType.CONE,
    newton.GeoType.CONVEX_MESH,
}


class Recorder:
    """Collect the variants of one tutorial; ``fps`` is the replay frame rate."""

    def __init__(self, fps: int = 30):
        parser = argparse.ArgumentParser()
        parser.add_argument(
            "--record", metavar="OUT.json", help="write a replay file for the tutorials page"
        )
        parser.add_argument("--device", default=None, help="Warp device, for example cuda:0")
        self.args = parser.parse_args()
        if self.args.device:
            wp.set_device(self.args.device)
        self.fps = fps
        self.script = os.path.abspath(sys.argv[0])
        self.variants: list[Variant] = []

    def frames(self, seconds: float) -> int:
        """Number of replay frames in ``seconds``, including the first."""
        return int(round(seconds * self.fps)) + 1

    def variant(
        self,
        label: str,
        model: newton.Model,
        *,
        ghost: bool = False,
        slot: int | None = None,
        scene: str | None = None,
    ) -> "Variant":
        """Start one run; ``ghost`` overlays run ``slot``, ``scene`` groups runs for charts."""
        variant = Variant(label, model, self.fps, ghost, slot, scene)
        self.variants.append(variant)
        return variant

    def save(self) -> None:
        """Print a summary and, with ``--record``, write the replay file."""
        for variant in self.variants:
            last = variant.metrics[-1] if variant.metrics else {}
            flag = "  (rows dropped)" if variant.overflow else ""
            print(f"{variant.label:<48} {json.dumps(last)}  {variant.wall:.2f} s{flag}")
        if not self.args.record:
            return
        source_sha = source_sha256(self.script)
        doc = {
            "script": os.path.basename(self.script),
            "script_sha256": source_sha,
            "newton_commit": _newton_commit(),
            "newton_version": newton.__version__,
            "warp_version": wp.config.version,
            "gpu": wp.get_device().name,
            "fps": self.fps,
            "variants": [v.to_json() for v in self.variants],
        }
        with open(self.args.record, "w") as f:
            json.dump(doc, f, separators=(",", ":"))
        print(f"wrote {self.args.record}")


class Variant:
    """One recorded run: a pose per frame for every body, plus named metric values."""

    def __init__(self, label, model, fps, ghost, slot, scene):
        self.label, self.model, self.fps, self.ghost, self.slot = label, model, fps, ghost, slot
        self.scene = scene
        self.poses: list[np.ndarray] = []
        self.metrics: list[dict] = []
        self.awake: list[list[int]] = []
        self.particles: list[np.ndarray] = []
        self.overflow = False
        self.start = time.perf_counter()
        self.wall = 0.0

    def frame(self, state: newton.State, awake=None, particles=None, **metrics) -> None:
        """Record ``state.body_q``, optional awake flags, particle positions and metric values."""
        self.poses.append(state.body_q.numpy().astype(np.float32).copy())
        if particles is not None:
            self.particles.append(particles.numpy().astype(np.float32).copy())
        self.metrics.append({k: _round(v) for k, v in metrics.items()})
        if awake is not None:
            self.awake.append([int(x) for x in awake.numpy()])
        self.wall = time.perf_counter() - self.start

    def check(self, solver) -> None:
        """Note whether the solver dropped rows; call once after the run."""
        try:
            solver.check_constraint_capacity()
        except RuntimeError:
            self.overflow = True

    def to_json(self) -> dict:
        poses = (
            np.stack(self.poses)
            if self.poses
            else np.zeros((0, self.model.body_count, 7), np.float32)
        )
        return {
            "label": self.label,
            "fps": self.fps,
            "ghost": self.ghost,
            "slot": self.slot,
            "scene": self.scene,
            "bodies": int(self.model.body_count),
            "frames": int(poses.shape[0]),
            "poses": base64.b64encode(poses.astype(np.float32).tobytes()).decode(),
            "shapes": _shape_table(self.model),
            "metric": self.metrics,
            "awake": self.awake or None,
            "particles": self._particles_json(),
            "wall_s": round(self.wall, 3),
            "overflow": self.overflow,
        }

    def _particles_json(self):
        if not self.particles:
            return None
        frames = np.stack(self.particles)
        tris = self.model.tri_indices
        radius = self.model.particle_radius.numpy()
        return {
            "count": int(frames.shape[1]),
            "frames": base64.b64encode(frames.astype(np.float32).tobytes()).decode(),
            "radius": round(float(radius.mean()), 5) if radius.size else 0.0,
            "tris": [int(v) for v in tris.numpy().reshape(-1)]
            if tris is not None and tris.shape[0]
            else None,
        }


def _shape_table(model):
    types = model.shape_type.numpy()
    scales = model.shape_scale.numpy()
    xforms = model.shape_transform.numpy()
    bodies = model.shape_body.numpy()
    flags = model.shape_flags.numpy()
    shapes = []
    for i, t in enumerate(types):
        if int(t) not in _RENDERED_TYPES or not flags[i] & int(newton.ShapeFlags.VISIBLE):
            continue
        shape = {
            "b": int(bodies[i]),
            "t": int(t),
            "s": [round(float(v), 5) for v in scales[i]],
            "x": [round(float(v), 6) for v in xforms[i]],
        }
        if int(t) in (newton.GeoType.MESH, newton.GeoType.CONVEX_MESH):
            mesh = model.shape_source[i]
            shape["v"] = [round(float(v), 5) for v in np.asarray(mesh.vertices).reshape(-1)]
            shape["i"] = [int(v) for v in np.asarray(mesh.indices).reshape(-1)]
        shapes.append(shape)
    return shapes


def _round(value):
    value = float(value)
    return round(value, 5) if np.isfinite(value) else None


def _newton_commit():
    if os.environ.get("NEWTON_COMMIT"):
        return os.environ["NEWTON_COMMIT"]
    try:
        root = os.path.dirname(os.path.dirname(newton.__file__))
        return subprocess.check_output(["git", "-C", root, "rev-parse", "HEAD"], text=True).strip()
    except (OSError, subprocess.CalledProcessError):
        return None

Recorded with SolverSteel on Newton 1.9.0.dev0, Warp 1.19.0, NVIDIA RTX PRO 6000 Blackwell Server Edition. Replays read the state back every frame, so they are not a speed benchmark. Script · page text