Can I use Kinfer within a Python environment?

Yes, Kinfer includes Python bindings via PyO3, allowing developers to load models and run inference within Python-based robotics scripts or research environments.

Which model formats does Kinfer support?

It primarily supports the ONNX format via the ort (ONNX Runtime) crate, ensuring compatibility with models trained in PyTorch, TensorFlow, and other major frameworks.

How does Kinfer ensure low latency for robotics?

It utilizes a highly optimized Rust implementation, a single Tokio runtime for async I/O, and input pre-fetching techniques to minimize the delay between sensor input and actuator command.

What is Kinfer Runtime?

Kinfer Runtime is a high-performance inference engine built in Rust for deploying trained Reinforcement Learning policies to physical robots using the ONNX format.

What kind of telemetry does the skill support?

The skill includes an asynchronous NDJSON logger that captures high-frequency telemetry data for debugging and visualization without interrupting the main control loop.

Kinfer Runtime

Name: Kinfer Runtime
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Deploys trained Reinforcement Learning (RL) policies to real robots using high-performance Rust and ONNX Runtime.

소개

Kinfer Runtime is a specialized model inference engine designed for the K-Scale robotics ecosystem, enabling the deployment of neural network policies directly onto physical hardware. By leveraging Rust and the ONNX Runtime (ort), it provides a low-latency pipeline essential for real-time robotics applications where timing is critical. It features optimized model loading, async telemetry logging via NDJSON, and seamless integration with KOS firmware, making it an essential tool for developers bridging the gap between simulated RL training and real-world robot execution.

주요 기능

2 GitHub stars
Cross-language support with Python bindings provided via PyO3.
High-performance ONNX model inference using Rust and the ort-sys backend.
Efficient asynchronous execution using a dedicated Tokio runtime with GIL management.
Asynchronous NDJSON telemetry logging for non-blocking performance debugging.
Real-time sensor-to-actuator execution loop optimized for embedded systems.

사용 사례

Executing real-time neural network inference on resource-constrained embedded hardware.
Deploying walking or manipulation policies from PyTorch/ONNX to physical humanoid robots.
Collecting high-frequency telemetry and sensor data for policy performance analysis.

소개

주요 기능

2 GitHub stars
Cross-language support with Python bindings provided via PyO3.
High-performance ONNX model inference using Rust and the ort-sys backend.
Efficient asynchronous execution using a dedicated Tokio runtime with GIL management.
Asynchronous NDJSON telemetry logging for non-blocking performance debugging.
Real-time sensor-to-actuator execution loop optimized for embedded systems.

사용 사례

Executing real-time neural network inference on resource-constrained embedded hardware.
Deploying walking or manipulation policies from PyTorch/ONNX to physical humanoid robots.
Collecting high-frequency telemetry and sensor data for policy performance analysis.