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>
29 lines
955 B
XML
29 lines
955 B
XML
<?xml version="1.0"?>
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<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
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<package format="3">
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<name>saltybot_first_encounter</name>
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<version>0.1.0</version>
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<description>
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First encounter orchestrator node for unknown person detection and enrollment.
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State machine: DETECT → GREET → ASK_NAME → SMALL_TALK → ENROLL → FAREWELL
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</description>
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<maintainer email="sl-controls@saltylab.local">sl-controls</maintainer>
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<license>MIT</license>
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<depend>rclpy</depend>
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<depend>std_msgs</depend>
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<depend>geometry_msgs</depend>
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<depend>sensor_msgs</depend>
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<buildtool_depend>ament_python</buildtool_depend>
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<test_depend>ament_copyright</test_depend>
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<test_depend>ament_flake8</test_depend>
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<test_depend>ament_pep257</test_depend>
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<test_depend>python3-pytest</test_depend>
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<export>
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<build_type>ament_python</build_type>
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</export>
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</package>
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