Skip to main content

HAL server implementation for Jetson with integrated inference

Project description

Krabby HAL Server - Jetson

HAL server implementation for Jetson robot deployment with integrated parkour policy inference.

Overview

This package provides an entry point that runs both the Jetson HAL server and parkour inference client in the same process using inproc ZMQ for zero-copy communication.

Architecture

┌─────────────────────────────────────────┐
│         Jetson Process                  │
│                                         │
│  ┌──────────────┐    inproc (ZMQ)     │
│  │ HAL Server   │◄──────────────────┐  │
│  │ (main thread)│                   │  │
│  │  - ZED camera│                   │  │
│  │  - Sensors   │                   │  │
│  │  - Actuators │                   │  │
│  └──────────────┘                   │  │
│         │                           │  │
│         │ publishes observations    │  │
│         │ receives commands         │  │
│         │                           │  │
│  ┌──────────────────────────────────┴─┐│
│  │ Parkour Inference Client           ││
│  │ (separate thread)                  ││
│  │  - Polls observations              ││
│  │  - Runs policy inference           ││
│  │  - Sends joint commands            ││
│  └────────────────────────────────────┘│
└─────────────────────────────────────────┘

Installation

From source (development)

cd hal/server/jetson
pip install -e .

With optional dependencies

pip install -e ".[dev]"

Usage

Command line

After installation, use the krabby-hal-server-jetson command:

krabby-hal-server-jetson \
  --checkpoint /path/to/model.pt

Python module

python -m hal.server.jetson.main --checkpoint /path/to/model.pt

Arguments

Required:

  • --checkpoint: Path to model checkpoint file

Optional:

  • --log-level: Python logging level for this process (DEBUG, INFO, WARNING, ERROR, CRITICAL; default: INFO)

Components

HAL Server (hal.server.jetson.JetsonHalServer)

  • Integrates with ZED camera for depth perception
  • Interfaces with real sensors (IMU, encoders)
  • Applies commands to actuators (motors)
  • Publishes observations via ZMQ PUB socket
  • Receives joint commands via ZMQ PULL socket

Parkour Inference Client (compute.parkour.inference_client.ParkourInferenceClient)

  • Runs in separate thread
  • Polls observations from HAL server
  • Runs parkour policy inference
  • Sends joint commands back to HAL server

Hardware Requirements

  • NVIDIA Jetson (Orin, AGX Xavier, or compatible)
  • ZED Camera (requires ZED SDK and pyzed)
  • Robot Hardware (motors, IMU, encoders)

Development

Project Structure

hal/server/jetson/
├── pyproject.toml      # Package configuration
├── README.md           # This file
├── __init__.py         # Package init
├── main.py             # Entry point with integrated inference
└── hal_server.py       # JetsonHalServer implementation

Running Tests

pytest tests/integration/test_jetson_hal.py

Note: Most tests require Jetson hardware or ZED SDK and are skipped in x86 environments.

Notes

  • This package uses inproc ZMQ by default for same-process communication (zero-copy, high performance)
  • For distributed deployment, use TCP endpoints instead
  • The parkour inference client runs on a separate thread to avoid blocking the sensor loop
  • Camera, sensors, and actuators are initialized during startup

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

krabby_hal_server_jetson-0.1.5-py3-none-any.whl (38.7 kB view details)

Uploaded Python 3

File details

Details for the file krabby_hal_server_jetson-0.1.5-py3-none-any.whl.

File metadata

File hashes

Hashes for krabby_hal_server_jetson-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 176e846618a7e8ae567f3295f035f264798f9fdd8813dc6f1942705d675ffd66
MD5 ec5bf69284375e25bae984250ad2c6a0
BLAKE2b-256 b9c4162774a3882ceb504e29e29771fe2fa32f5fa74b283be23f8046831afb41

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page