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space-autonomy-quantum

Autonomous space navigation agent using optical quantum kernels for terrain classification.

aadipapp0 installations selon la source☆ 0 Étoiles du dépôtv0.1.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install aadipapp/space-autonomy-skill

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Space Autonomy Quantum Skill

This skill simulates an autonomous agent for space exploration that uses Optical Quantum Kernels to classify terrain. It emphasizes highest safety by implementing strict confidence thresholds. If the quantum classifier is uncertain, the agent triggers a failsafe "SAFE MODE".

Features

  • Quantum Perception: Uses simulated optical quantum interference to recognize terrain features.
  • Safety Failsafe: Automatically halts if classification confidence is below 0.8.
  • Autonomous Decision Making: Decides to "Navigate" or "Avoid" based on quantum kernel results.

Commands

  • navigate: Process a sensor reading and decide on an action.

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/aadipapp/space-autonomy-skill
Version
0.1.0
Commit
00afc94bca7b9307b6c6c4991144f3ed11507f63
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

Space Autonomy Quantum Skill

This skill simulates an autonomous agent for diverse space environments. It relies on Optical Quantum Kernels to classify terrain based on sensor data.

Safety First

Space is unforgiving. This agent implements a "Highest Safety" protocol:

  1. Failsafe Threshold: If the quantum classifier's confidence is below 0.85, the agent immediately triggers SAFE MODE.
  2. Redundancy: Every classification is computed 3 times and averaged to mitigate quantum/optical noise.
  3. Default to Halt: If a hazard is detected OR if the terrain is unknown, the agent stops.

How it Works

The agent compares real-time sensor inputs against a "Knowledge Base" (Safe Ground, Rocks, Voids) using a simulated optical quantum computer.

  • High Kernel Value -> High Similarity -> Confident Classification.
  • Low Kernel Value -> Low Similarity -> Unknown Terrain -> Safe Mode.

Usage

Simulate a sensor reading (vector of 3 values):

Case 1: Clear, safe terrain (matches 'SAFE_FLAT')

python3 scripts/quantum_nav.py "0.1,0.12,0.1"
# Expected: Action: PROCEED

Case 2: Hazardous Rock (matches 'HAZARD_ROCK')

python3 scripts/quantum_nav.py "0.8,0.9,0.7"
# Expected: Action: AVOID / HALT

Case 3: Unknown/Ambiguous Signal

python3 scripts/quantum_nav.py "0.5,0.5,0.5"
# Expected: >>> TRIGGERING FAILSAFE: SAFE MODE <<<

Publishing

clawhub publish

Contenu extrait

SKILL.md
# Space Autonomy Quantum Skill

This skill simulates an autonomous agent for space exploration that uses **Optical Quantum Kernels** to classify terrain.
It emphasizes **highest safety** by implementing strict confidence thresholds. If the quantum classifier is uncertain, the agent triggers a failsafe "SAFE MODE".

## Features
- **Quantum Perception**: Uses simulated optical quantum interference to recognize terrain features.
- **Safety Failsafe**: Automatically halts if classification confidence is below 0.8.
- **Autonomous Decision Making**: Decides to "Navigate" or "Avoid" based on quantum kernel results.

## Commands

- `navigate`: Process a sensor reading and decide on an action.
README
# Space Autonomy Quantum Skill

This skill simulates an autonomous agent for diverse space environments. It relies on **Optical Quantum Kernels** to classify terrain based on sensor data.

## Safety First
Space is unforgiving. This agent implements a **"Highest Safety"** protocol:
1.  **Failsafe Threshold**: If the quantum classifier's confidence is below `0.85`, the agent immediately triggers **SAFE MODE**.
2.  **Redundancy**: Every classification is computed 3 times and averaged to mitigate quantum/optical noise.
3.  **Default to Halt**: If a hazard is detected OR if the terrain is unknown, the agent stops.

## How it Works
The agent compares real-time sensor inputs against a "Knowledge Base" (Safe Ground, Rocks, Voids) using a simulated optical quantum computer.
- **High Kernel Value** -> High Similarity -> Confident Classification.
- **Low Kernel Value** -> Low Similarity -> Unknown Terrain -> Safe Mode.

## Usage

Simulate a sensor reading (vector of 3 values):

# Case 1: Clear, safe terrain (matches 'SAFE_FLAT')
```bash
python3 scripts/quantum_nav.py "0.1,0.12,0.1"
# Expected: Action: PROCEED
```

# Case 2: Hazardous Rock (matches 'HAZARD_ROCK')
```bash
python3 scripts/quantum_nav.py "0.8,0.9,0.7"
# Expected: Action: AVOID / HALT
```

# Case 3: Unknown/Ambiguous Signal
```bash
python3 scripts/quantum_nav.py "0.5,0.5,0.5"
# Expected: >>> TRIGGERING FAILSAFE: SAFE MODE <<<
```

## Publishing
```bash
clawhub publish
```

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