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.