This project is in active beta development. This means the API is unstable, features may be added or removed, and breaking changes are likely to occur frequently and without notice as the design is refined.

Newton Actuators

GPU-accelerated actuator library for physics simulations.

This library provides a collection of actuator implementations that integrate with physics simulation pipelines. Actuators read from simulation state arrays and write computed forces/torques back to control arrays.

Installation

pip install newton-actuators

Or install from source:

cd newton-actuators
pip install -e .

API Reference

Actuator Classes

Actuator	Description	Stateful	Transmission
ActuatorPD	Stateless PD controller	No	No
ActuatorPID	PID controller with integral term	Yes	No
ActuatorDelayedPD	PD controller with input delay	Yes	No

Control Laws

• ActuatorPD: τ = clamp(constant + act + Kp*e_pos + Kd*e_vel, ±max_force)
• ActuatorPID: τ = clamp(constant + act + Kp*e + Ki*∫e·dt + Kd*ė, ±max_force)
• ActuatorDelayedPD: Same as PD but with delayed targets (circular buffer)

Base Class Methods

All actuators inherit from Actuator and provide these methods:

• resolve_arguments(args) -> dict: (classmethod) Resolve user-provided arguments with defaults
• is_stateful() -> bool: Returns True if the actuator maintains internal state
• has_transmission() -> bool: Returns True if the actuator has a transmission phase
• state() -> State | None: Returns a new state instance (None for stateless actuators)
• step(sim_state, sim_control, current_state, next_state, dt): Execute one control step

State Classes

Stateful actuators use nested State classes:

• ActuatorPID.State - Contains the integral term for PID control
• ActuatorDelayedPD.State - Contains circular buffers for delayed targets

Workflow

1. Create actuators with appropriate parameters
2. Check statefulness: Call actuator.is_stateful() to determine if state management is needed
3. Initialize states: For stateful actuators, create double-buffered states with actuator.state()
4. Simulation loop: Call actuator.step() to compute forces
5. Swap buffers: For stateful actuators, swap state buffers after each step

Examples

Stateless Actuator (ActuatorPD)

import warp as wp
from newton_actuators import ActuatorPD

# Create a PD actuator for 3 DOFs
indices = wp.array([0, 1, 2], dtype=wp.uint32)
pd_actuator = ActuatorPD(
    input_indices=indices,
    output_indices=indices,
    kp=wp.array([100.0, 100.0, 100.0], dtype=wp.float32),
    kd=wp.array([10.0, 10.0, 10.0], dtype=wp.float32),
    max_force=wp.array([50.0, 50.0, 50.0], dtype=wp.float32),
    constant_force=wp.array([0.0, 0.0, 0.0], dtype=wp.float32),
)

# In simulation loop - stateless actuators don't need state management
pd_actuator.step(sim_state, sim_control, None, None, dt=0.01)

Stateful Actuator (ActuatorPID)

import warp as wp
from newton_actuators import ActuatorPID

indices = wp.array([0, 1], dtype=wp.uint32)
pid_actuator = ActuatorPID(
    input_indices=indices,
    output_indices=indices,
    kp=wp.array([100.0, 100.0], dtype=wp.float32),
    ki=wp.array([10.0, 10.0], dtype=wp.float32),
    kd=wp.array([5.0, 5.0], dtype=wp.float32),
    max_force=wp.array([50.0, 50.0], dtype=wp.float32),
    integral_max=wp.array([10.0, 10.0], dtype=wp.float32),
    constant_force=wp.array([0.0, 0.0], dtype=wp.float32),
)

# Check if actuator needs state management
if pid_actuator.is_stateful():
    # Create double-buffered states
    state_a = pid_actuator.state()
    state_b = pid_actuator.state()

# Simulation loop with state swapping
current_state, next_state = state_a, state_b
for step in range(num_steps):
    pid_actuator.step(sim_state, sim_control, current_state, next_state, dt=0.01)
    current_state, next_state = next_state, current_state  # Swap buffers

USD Parsing

The library includes utilities for parsing actuator definitions from USD files:

from newton_actuators import parse_actuator_prim

# Parse a USD prim with actuator attributes
result = parse_actuator_prim(prim)
if result is not None:
    actuator_class = result.actuator_class  # e.g., ActuatorPD
    target_paths = result.target_paths      # e.g., ["/World/Robot/Joint1"]
    kwargs = result.kwargs                   # e.g., {"kp": 100.0, "kd": 10.0}

License

Apache-2.0