Torsional friction on articulated bodies

07 · Contact and friction

A heavy ball on a vertical slider and a vertical hinge spins at 20 rad/s on the ground. On articulated bodies, torsional friction comes from a per-shape effective radius r, the PhysX minTorsionalPatchRadius. The spin row is bounded by r·(μN − sliding), so spin uses whatever Coulomb budget sliding leaves over.

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What to look at

With r = 0 the ball spins forever.
With r = 1 cm it stops in about half a second, and r = 5 cm stops it five times faster.

Key parameter

steel:min_torsional_patch_radius

Per shape, in metres. For uniform pressure on a disk of radius R use 2R/3. Prepare the rows on the GPU with contact_torsion_device=True so the step can be captured in a CUDA graph.

Try changing

Set the ground's μ to 0.2.
Try contact_torsion_device=False, the host reference path.

Concept

Torsional friction budget

What it is

On articulated links, spin friction is one extra row per contact group about the group normal. Its bound shares the Coulomb budget with sliding, r·(μN − sliding), where r is an effective radius per shape (for uniform pressure on a disk of radius R, r = 2R/3).

In this scene

The hinged ball spinning at 20 rad/s keeps spinning with r = 0, stops in about 0.5 s with r = 1 cm, and in about 0.1 s with r = 5 cm.

Considerations

Cost: One row per contact group; contact_torsion_device=True builds them on the GPU and keeps the step capturable.
Interactions: Requires contact_warmstart=False and velocity_iterations=0; hydroelastic contacts are rejected.
Failure modes: Spin rows carry no history, so very slow residual spin can creep.
Use it when: Grippers and screwdrivers where twisting a held object must resist torque.

The script

cd tutorials && python 07_articulated_torsion.py prints each variant's final metric; --record out.json also writes the replay. Every tutorial imports replay.py from the same folder.

07_articulated_torsion.py
replay.py

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

"""Torsional friction on articulated contacts with steel:min_torsional_patch_radius."""

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


def build(radius):
    builder = newton.ModelBuilder()
    SolverSteel.register_custom_attributes(builder)
    builder.add_ground_plane(cfg=newton.ModelBuilder.ShapeConfig(mu=0.6))
    # a massless vertical slider carries a ball on a vertical hinge
    slider = builder.add_link(xform=wp.transform((0.0, 0.0, 0.06), wp.quat_identity()))
    j0 = builder.add_joint_prismatic(
        -1,
        slider,
        axis=newton.Axis.Z,
        parent_xform=wp.transform((0.0, 0.0, 0.06), wp.quat_identity()),
        armature=0.01,
    )
    ball = builder.add_link(xform=wp.transform((0.0, 0.0, 0.06), wp.quat_identity()))
    builder.add_shape_sphere(
        ball, radius=0.06, cfg=newton.ModelBuilder.ShapeConfig(density=3000.0, mu=0.6)
    )
    marker = newton.ModelBuilder.ShapeConfig(density=0.0, has_shape_collision=False)
    builder.add_shape_box(
        ball,
        xform=wp.transform((0.0, 0.0, 0.06), wp.quat_identity()),
        hx=0.05,
        hy=0.008,
        hz=0.004,
        cfg=marker,
    )
    j1 = builder.add_joint_revolute(slider, ball, axis=newton.Axis.Z)
    builder.add_articulation([j0, j1])
    model = builder.finalize()
    model.steel.min_torsional_patch_radius.fill_(radius)
    model.joint_qd.assign(np.array([0.0, 20.0], dtype=np.float32))
    return model


def simulate(rec, label, radius):
    model = build(radius)
    solver = SolverSteel(model, contact_torsion_device=True, friction_anchor_beta=0.0)
    pipeline = newton.CollisionPipeline(model)
    contacts = pipeline.contacts()
    state_0, state_1, control = model.state(), model.state(), model.control()

    run = rec.variant(label, model)
    for _ in range(rec.frames(4.0)):
        run.frame(state_0, spin=state_0.joint_qd.numpy()[1])
        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, "radius 0 (no torsion, default)", 0.0)
    simulate(rec, "radius 1 cm", 0.01)
    simulate(rec, "radius 5 cm", 0.05)
    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 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

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.