sl-controls
c85619b8da
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Implement state machine for detecting and enrolling unknown persons.
Manages workflow: DETECT → GREET → ASK_NAME → SMALL_TALK → ENROLL → FAREWELL
Features:
- Subscribes to /saltybot/person_tracker for unknown face detection
- Unknown person threshold configurable (default: 30% confidence)
- State machine with Piper TTS triggers for each state
- Captures STT responses for name and conversation context
- Publishes /social/orchestrator/state for coordination with other nodes
- Handles person interruptions gracefully (walks away)
- Auto-enrolls person to face gallery (configurable)
- Stores encounter data as JSON in /home/seb/encounter-queue/
- Tracks duration, responses, interests, and enrollment success
Encounter data structure:
{
person_id, timestamp, state, name, context, greeting_response,
interests[], enrollment_success, duration_sec, notes
}
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
28 lines
875 B
Python
28 lines
875 B
Python
from setuptools import setup
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package_name = "saltybot_first_encounter"
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setup(
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name=package_name,
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version="0.1.0",
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packages=[package_name],
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data_files=[
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("share/ament_index/resource_index/packages", [f"resource/{package_name}"]),
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(f"share/{package_name}", ["package.xml"]),
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(f"share/{package_name}/launch", ["launch/first_encounter.launch.py"]),
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(f"share/{package_name}/config", ["config/encounter_params.yaml"]),
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],
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install_requires=["setuptools"],
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zip_safe=True,
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maintainer="sl-controls",
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maintainer_email="sl-controls@saltylab.local",
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description="First encounter state machine orchestrator",
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license="MIT",
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tests_require=["pytest"],
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entry_points={
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"console_scripts": [
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"first_encounter_node = saltybot_first_encounter.first_encounter_node:main",
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],
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},
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)
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