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genpark-embodied-trajectory-quintic-spline-smoother-skill

mcp
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SUMMARY

GenPark Physical AI quintic polynomial trajectory smoother with continuous boundary constraints (position, velocity, acceleration), and analytic jerk minimization.

README.md

genpark-embodied-trajectory-quintic-spline-smoother-skill

Organization
Model Context Protocol
Zero-Dependency
Python Version
License

Production-grade Physical AI & Robotics skill engineered strictly with Python standard library. Provides real-time spatial intelligence, trajectory generation, and tactile control for autonomous robotic systems via the Model Context Protocol (MCP).

Architecture & Data Flow

flowchart TD
    Sensor[3D Depth Sensors / Joint Encoders / Tactile Array] --> Ingest[Telemetry Normalizer]
    Ingest --> Core["GenParkEmbodiedTrajectoryQuinticSplineSmoother"]
    Core --> Kinematics[Robotics Dynamics & Spatial Engine]
    Kinematics --> Safety[Boundary & Collision Guardrails]
    Safety --> MCPOutput[Standard MCP Tool Result / Control Vector]

Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Standalone MCP Server Launch

Run the native STDIO MCP server:

python mcp_server.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

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