README.md

metadata

title: ghostloop demo
emoji: 🤖
colorFrom: green
colorTo: indigo
sdk: gradio
sdk_version: 5.12.0
python_version: '3.12'
app_file: app.py
pinned: true
license: mit
short_description: Drive any robot through a fail-closed safety pipeline.
tags:
  - robotics
  - embodied-ai
  - agent
  - mcp
  - safety
  - mujoco
  - ros2

ghostloop · live demo

The agent loop, embodied. Tool-using runtime + fail-closed safety pipeline + sim-first execution + post-hoc analysis layer for embodied AI / robotics. Sister project to GhostLM.

This Space lets you pick a robot profile (Franka arm / Spot quadruped / Tello drone / Stretch mobile arm / humanoid / TurtleBot) and dispatch Intents through the safety pipeline that ships in the library.

• GitHub: https://github.com/joemunene-by/ghostloop
• PyPI: pip install ghostloop
• arXiv: [link to be added once preprint is up]

What you can try here

1. Switch profiles to see how the same Runtime + safety pipeline shape covers totally different morphologies.
2. Send move_to {"x": 5, "y": 0, "z": 0} on the Franka profile to watch the GeofenceGate reject it with a structured reason.
3. Send takeoff {"altitude": 1} on the Tello profile to see the HITL gate escalate.
4. Read the trace event JSON for any call. That's the same shape the library emits for replay, diff, query, energy ledger, and judge scoring.

Beyond the demo

The full library does much more than this Space exposes:

• 6 backends (Mock / MuJoCo / PyBullet / Gymnasium / ROS 2 / Randomized).
• 12 policy gates including STL temporal properties.
• Counterfactual trace replay, causal failure attribution, LLM-as-judge.
• VLA-on-MuJoCo benchmark harness vs OpenVLA / π0 / RT-2 / Octo numbers.
• Safe-RL training loop with Lagrangian multiplier + HER.
• Production fleet dashboard with auth + rate limit + alarms + Prometheus.
• MCP server for Claude Desktop / Cursor / Continue / Cline / Zed / Gemini CLI.

pip install ghostloop and clone the repo for the full kit.