Joint velocity limits
33 · Joints and drives
A flywheel under a constant 3 N·m torque with a 6 rad/s velocity limit. Without enable_joint_velocity_limits it keeps accelerating. With it, Steel scales velocities that already exceed a limit and adds a pair of clamp rows per limited DOF.
What to look at
- Off: the flywheel spins up to about 180 rad/s in 3 s.
- On: it holds 6 rad/s.
Key parameter
enable_joint_velocity_limitsUses Model.joint_velocity_limit (add_joint_* velocity_limit). velocity_limit_activation_fraction creates the rows only near the limit.
Try changing
- Set velocity_limit_activation_fraction=0.9.
- Use drive_mode="rows" with a velocity target above the limit.
Concept
Joint velocity limits
What it is
Steel first scales articulation velocities that already exceed a limit, then adds a pair of clamp rows per limited DOF so the solve cannot exceed it.
In this scene
Under a constant 3 N·m the flywheel reaches 179 rad/s in 3 s without limits and holds 6.0 rad/s with them.
Considerations
- Cost
- Two rows per limited DOF; velocity_limit_activation_fraction creates them only near the limit.
- Accuracy
- The gate samples velocity before the solve, so crossing within a step is clamped a step late.
- Use it when
- Motors with rated speeds.
The script
cd tutorials && python 33_joint_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.
# SPDX-FileCopyrightText: Copyright (c) 2026 Skild AI and the Steel contributors# SPDX-License-Identifier: Apache-2.0
"""Joint velocity limits: a flywheel under constant torque."""
import newtonimport numpy as npimport warp as wpfrom newton.solvers import SolverSteel
from replay import Recorder
DT = 1.0 / 240.0SUBSTEPS = 8
def build(): builder = newton.ModelBuilder(gravity=(0.0, 0.0, 0.0)) hub = builder.add_link(xform=wp.transform((0.0, 0.0, 0.5), wp.quat_identity())) builder.add_shape_cylinder( hub, radius=0.2, half_height=0.02, cfg=newton.ModelBuilder.ShapeConfig(density=500.0) ) builder.add_shape_box( hub, xform=wp.transform((0.15, 0.0, 0.03), wp.quat_identity()), hx=0.04, hy=0.02, hz=0.01, cfg=newton.ModelBuilder.ShapeConfig(density=50.0), ) j = builder.add_joint_revolute( -1, hub, axis=newton.Axis.Z, parent_xform=wp.transform((0.0, 0.0, 0.5), wp.quat_identity()), velocity_limit=6.0, ) builder.add_articulation([j]) return builder.finalize()
def simulate(rec, label, **options): model = build() solver = SolverSteel(model, **options) pipeline = newton.CollisionPipeline(model) contacts = pipeline.contacts() state_0, state_1, control = model.state(), model.state(), model.control() control.joint_f.assign(np.array([3.0], dtype=np.float32)) # 3 N m, constant
run = rec.variant(label, model) for _ in range(rec.frames(3.0)): run.frame(state_0, omega=state_0.joint_qd.numpy()[0]) for _ in range(SUBSTEPS): pipeline.collide(state_0, contacts) solver.step(state_0, state_1, control, contacts, DT) state_0, state_1 = state_1, state_0 run.check(solver)
if __name__ == "__main__": rec = Recorder() simulate(rec, "velocity limits off (default)") simulate(rec, "enable_joint_velocity_limits=True", enable_joint_velocity_limits=True) rec.save()# 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 printsthe final metric of each; ``--record out.json`` also writes body poses, shapes and metricseries that the page replays in 3D."""
import argparseimport base64import jsonimport osimport subprocessimport sysimport time
import newtonimport numpy as npimport 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 NoneRecorded 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
Steel docs and tutorials are licensed under Apache-2.0. Third-party notices. "Skild", "Skild AI" and the Skild logo are trademarks of Skild AI and are not licensed under Apache-2.0.