Newton

Newton#

Newton is a Warp-native backend that can run without Isaac Sim. Its Isaac Lab integration supports multiple solver families. Configure the backend with NewtonCfg and choose a solver configuration through its solver_cfg field.

Check task --help or the environment catalog for supported presets. Before tuning a new asset, follow Prepare an Asset for Newton with MJWarp.

Solver options#

Use the links below for Isaac Lab configuration and tuning. Newton’s solver guide explains the algorithms, features, and contact models. Isaac Lab’s API references list configuration fields and defaults.

Isaac Lab configuration

Workflow

MJWarpSolverCfg

Tune MJWarp for the primary validated solver path

KaminoPADMMSolverCfg and KaminoDVISolverCfg

Tune Kamino for Kamino configuration

VBDSolverCfg

VBD Solver for cloth, soft bodies, and coupled scenes

MPMSolverCfg

Implicit MPM for how the integration works and MPM Solver for parameter studies

Additional solver configurations are XPBDSolverCfg and FeatherstoneSolverCfg. Use their generated API references and the Newton solver guide when evaluating a task-specific preset.

Native access#

Use native access when the shared asset and sensor APIs do not expose the data or operation you need. Reuse an Isaac Lab-owned handle when its selection matches; create raw access only for additional capabilities. Native writes can bypass Isaac Lab’s caches and validation, so follow the ownership and synchronization rules below.

Mental model#

The Newton backend exposes its engine-owned Model, State, Control, and optional Contacts objects through isaaclab_newton.physics.NewtonManager. Their Warp arrays are the live engine data, rather than values pulled into a separate per-asset view buffer. The model owns structural and static arrays and labels; a state owns evolving simulation arrays; and a control owns actuation inputs. Contacts are optional and depend on the active solver and collision path.

Lifecycle prerequisite#

Access Newton data after the simulation context has initialized the physics backend and built its model. Treat the objects as invalid after a model rebuild and reacquire them after Isaac Lab has reinitialized the simulation. This API is intended for code that can own the necessary lifecycle and synchronization responsibilities.

Reuse Isaac Lab-owned access#

Use the manager accessors to obtain the current Newton objects:

from isaaclab_newton.physics import NewtonManager

model = NewtonManager.get_model()
state = NewtonManager.get_state_0()
control = NewtonManager.get_control()
contacts = NewtonManager.get_contacts()

body_poses = state.body_q
joint_forces = control.joint_f

isaaclab_newton.physics.NewtonManager.get_model() returns the active Newton physics model, or None before the Newton backend has built it; render-only models used by visualizers are not returned here. The write semantics in this section apply only when Newton is the active, authoritative physics backend.

Isaac Lab’s Newton-backed assets expose the same generic newton.selection.ArticulationView selection helper. For example, reuse an articulation’s root selection instead of constructing the matching selection again:

robot = scene["robot"]
selection = robot.root_view
joint_positions = selection.get_dof_positions(state)

Newton uses this generic, label-based selection concept for Isaac Lab articulations, rigid objects, rigid-object collections, and cables. It is a selection helper over model indices, not per-asset typed storage.

Create raw access#

Code that owns a matching model can construct its own selection from a model and a label pattern:

from newton.selection import ArticulationView

selection = ArticulationView(
    model,
    pattern="/World/envs/env_*/Robot",
)

Read/write semantics#

Selections provide typed convenience methods as well as generic string-keyed get_attribute() and set_attribute() methods. The generic methods expose engine properties that do not have dedicated selection methods. Clone a selected value before modifying it when you want the write to remain explicit:

import warp as wp
from newton import ModelFlags
from isaaclab_newton.physics import NewtonManager

rolling_friction = wp.clone(
    selection.get_attribute("shape_material_mu_rolling", model)
)
# Modify rolling_friction with a Warp kernel before writing it back.
selection.set_attribute(
    "shape_material_mu_rolling",
    model,
    rolling_friction,
)
NewtonManager.add_model_change(ModelFlags.SHAPE_PROPERTIES)

The string names a Newton model attribute rather than an Isaac Lab field. This example uses rolling friction because it has no dedicated selection method. Notify the manager with the flag appropriate to the property changed; other model writes can require a different flag. State writes that change generalized coordinates instead require forward-kinematics synchronization through NewtonManager.invalidate_fk() and NewtonManager.forward().

Ownership, synchronization, and invalidation#

Direct writes bypass Isaac Lab caches and shape/order validation. Some Newton solvers swap current and next state buffers, so reacquire isaaclab_newton.physics.NewtonManager.get_state_0() when code needs the current authoritative state on a later step. A selection can survive state-buffer swaps because it describes model indices, but it must be recreated after a model rebuild. Solver-specific generalized- and maximal-coordinate conventions remain authoritative.

Authoritative references#