Willow 5 Runtime: Action Recognition, Retargeting, & Edge Evaluator
Project description
Willow 5 Runtime SDK (Python)
The Willow 5 Runtime SDK is the local execution environment for the Willow Dynamics platform.
While the Willow Cloud Oracle is used to train and generate proprietary Action Models, this SDK allows you to download and execute those models locally on your own hardware. It acts as the bridge between Willow's biomechanical intelligence and your edge devices, robotics, or reinforcement learning simulations.
Core Capabilities
- Dual Spatial Engines: Natively decodes V40 (Scale-Invariant) and V41 (Absolute Physics/Metric) signatures.
- Object Tracking (Node 76): Natively process human-object interactions (tools, balls) in a unified 3D metric space.
- Local Execution: Run Zero-Shot Action Recognition entirely on the edge using ultra-compressed
.int8binaries. - Sim-to-Real Retargeting: Convert Computer Vision coordinates into ROS (Z-Up) and Unity formats natively.
- Edge Physics: Calculate Jerk, Acceleration, and Power without cloud latency using the ported Da Vinci engine.
- DRM-Compliant: Models can load securely into ephemeral RAM to protect Intellectual Property.
Spatial Engine Modes (New in Q2 2026)
The SDK automatically parses the binary header of your model to determine its mathematical routing:
- Universal Models (Scale-Invariant): Evaluates structural form and sequence. Distances are mathematically normalized by torso length, allowing a 5-foot subject and a 6-foot-5 subject to yield a 100% match. Expects a
(75, 3)generic coordinate array. - Physics Models (Metric Space): Evaluates exact volumetric paths and physical velocities (m/s). Normalization is bypassed. Expects coordinates to be provided in absolute Meters, utilizing a
(76, 3)array where Index 75 represents the tracked reference object.
Installation
pip install willow-runtime
Configuration
To fetch models, you must initialize the WillowClient with credentials provided in your Partner Dashboard:
- API URL: The endpoint of your dedicated Willow Gateway.
- API Key: Your secure access token (authenticates the connection).
- Customer ID: Your specific Tenant ID (scopes the data access).
from willow import WillowClient
client = WillowClient(
api_url="https://api.your-gateway.com",
api_key="sk_live_...",
customer_id="cust_12345"
)
Usage Guide
1. Active Streaming (Real-Time)
Best for Robotics, Webcams, or Smart Gyms where frames arrive one by one. The step() method manages an internal sliding window buffer for zero-latency triggering.
from willow import WillowClient, WillowDetector
# 1. Load Model (Secure RAM Stream)
client = WillowClient(api_url="...", api_key="...", customer_id="...")
model = client.get_model("tactical-reload-v1")
detector = WillowDetector(model)
# 2. The Real-Time Loop
# Assume 'get_next_frame()' returns a (75, 3) or (76, 3) numpy array
while True:
current_skeleton, timestamp_ms = get_next_frame()
# .step() executes in <2ms
event = detector.step(current_skeleton, timestamp_ms)
if event:
print(f"Action Detected at {event['end_ms']}ms | Confidence: {event['confidence']:.2f}")
# Trigger Actuator / Robot / UI here
2. Batch Analysis (Passive)
Best for Data Science and Historical Video Processing. Processes an entire sequence array at once.
from willow import WillowDetector, load_local_model
import numpy as np
# Load model from disk (Offline Mode)
model = load_local_model("models/golf-swing-v4.int8")
detector = WillowDetector(model)
# Input: (Frames, 75 or 76, 3)
matches = detector.detect(full_sequence, timestamps)
print(f"Found {len(matches)} events.")
3. Robotics & Sim-to-Real
Bridges the gap between Computer Vision coordinates and Robotics standards for NVIDIA Isaac Sim or ROS.
from willow import CoordinateBridge, KinematicRetargeter
# 1. Convert Coordinate Space
# MediaPipe (Y-Down) -> ROS/Isaac Sim (Z-Up)
ros_ready_sequence = CoordinateBridge.to_ros_z_up(raw_skeleton_sequence)
# 2. Extract Joint Angles for RL Training
angles = KinematicRetargeter.extract_full_body_quaternions(ros_ready_sequence)
print(f"Spine Quaternion: {angles['spine']}")
Documentation Sections
- Architecture & Best Practices - Understand how the Willow system functions. -Model Provisioning - How to fetch, cache, and manage secure models.
- Coordinate Transforms - Bridging Willow to ROS, Unity, and Unreal.
- Robotics & Retargeting - Extracting Quaternions for RL & Simulation.
- Physics Evaluation - Scoring speed, smoothness, and impulse at the edge. -Topology Map - The Willow 76-point joint index reference.
Support & Licensing
The Willow Runtime is a premium commercial service. A Partner License is required to provision models. -Request a License
© 2026 Willow Dynamics. All rights reserved.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file willow_runtime-5.2.8.tar.gz.
File metadata
- Download URL: willow_runtime-5.2.8.tar.gz
- Upload date:
- Size: 18.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
753ed66609def3dcf42ea41f78462e9f0fcced073b48e414823fd5fc93b5d645
|
|
| MD5 |
2cd74ff9af951a18a222ee35cd9709d2
|
|
| BLAKE2b-256 |
750cca345534b476e310e673237332bd14b6b0947d802f92a62e5ca06ae4a2a4
|
File details
Details for the file willow_runtime-5.2.8-py3-none-any.whl.
File metadata
- Download URL: willow_runtime-5.2.8-py3-none-any.whl
- Upload date:
- Size: 17.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
de7fe3ca0474f80008f75ea7e279ac2dc996a4806495cb8c2c5b2f91e563986b
|
|
| MD5 |
8206c9f82eb5615bbd6f1ee0487b5610
|
|
| BLAKE2b-256 |
7ecf491fbd9980c3a232ea4aa8274f84642ba97a0b93bd4b227374cef5b62943
|