Friction combine modes

04 · Contact and friction

Boxes with μ = 0.1, 0.4 and 0.9 sit on a 25° ramp with μ = 0.4 (tan 25° ≈ 0.47). Each contact needs one coefficient for a pair of shapes, and friction_combine picks how to combine them, with the same modes as PhysX: average (default), min, multiply or max. The same boxes stick or slide depending on the mode.

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What to look at

With average, only the μ = 0.9 box holds (0.65 > 0.47).
With max, the μ = 0.4 box slides too (max is 0.4 < 0.47), and the μ = 0.9 box holds.
With min and multiply every box slides.

Key parameter

friction_combine

"average", "min", "multiply" or "max" of the two shapes' mu.

Try changing

Set RAMP_MU = 1.0 and compare average and min.
Use restitution_combine the same way for bounce.

Concept

Combining material coefficients

What it is

A contact needs one friction coefficient, but each of its two shapes has one. friction_combine picks the rule: average (default), min, multiply or max, the modes PhysX offers. restitution_combine does the same for bounce.

In this scene

With a ramp μ of 0.4, the μ = 0.9 box holds under average (0.65) and max, but slides under min and multiply (0.36), so the same scene gives three different outcomes.

Considerations

Cost: None measurable: one lookup per contact.
Accuracy: Average and multiply make a high-friction shape slippery on a low-friction one; max and min let one material decide.
Interactions: contact_friction_scale scales each shape's coefficient before combining, so multiply squares a common scale. Patches only form between contacts whose combined coefficients match.
Use it when: Match the source of your assets: PhysX scenes often author a combine mode per material.

The script

cd tutorials && python 04_friction_combine.py prints each variant's final metric; --record out.json also writes the replay. Every tutorial imports replay.py from the same folder.

04_friction_combine.py
replay.py

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

"""Friction combine modes: how two shapes' mu values become one contact coefficient."""

import math

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
SLOPE = math.radians(25.0)  # tan(25 deg) = 0.47
BOX_MU = [0.1, 0.4, 0.9]
RAMP_MU = 0.4


def build():
    builder = newton.ModelBuilder()
    builder.add_ground_plane()
    tilt = wp.quat_from_axis_angle(wp.vec3(0.0, 1.0, 0.0), SLOPE)
    builder.add_shape_box(
        -1,
        xform=wp.transform((0.0, 0.0, 0.4), tilt),
        hx=0.9,
        hy=0.45,
        hz=0.03,
        cfg=newton.ModelBuilder.ShapeConfig(mu=RAMP_MU),
    )
    normal = np.array(wp.quat_rotate(tilt, wp.vec3(0.0, 0.0, 1.0)))
    uphill = np.array(wp.quat_rotate(tilt, wp.vec3(1.0, 0.0, 0.0)))
    for i, mu in enumerate(BOX_MU):
        p = np.array([0.0, 0.3 * i - 0.3, 0.4]) + 0.5 * uphill + 0.071 * normal
        box = builder.add_body(xform=wp.transform(p, tilt))
        builder.add_shape_box(
            box, hx=0.04, hy=0.04, hz=0.04, cfg=newton.ModelBuilder.ShapeConfig(mu=mu)
        )
    return builder.finalize()


def simulate(rec, mode):
    model = build()
    solver = SolverSteel(model, friction_combine=mode)
    pipeline = newton.CollisionPipeline(model)
    contacts = pipeline.contacts()
    state_0, state_1, control = model.state(), model.state(), model.control()
    start = state_0.body_q.numpy()[:, :3].copy()

    run = rec.variant(f'friction_combine="{mode}"', model)
    for _ in range(rec.frames(2.0)):
        slide = np.linalg.norm(state_0.body_q.numpy()[:, :3] - start, axis=1)
        run.frame(state_0, **{f"mu_{mu}": d for mu, d in zip(BOX_MU, slide)})
        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()
    for mode in ["average", "min", "multiply", "max"]:
        simulate(rec, mode)
    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.