This repository contains code and simulation workflows for adaptive gait control of a sea turtle–inspired robot using Hopf Central Pattern Generators (CPGs) combined with Bayesian Optimization. The project investigates how AI-based optimization can improve locomotion efficiency, adaptability, and robustness across varying terrain conditions.
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Central Pattern Generator (CPG) Control
- Implements Hopf oscillator–based rhythmic control for multi-joint locomotion.
- Supports multiple gait patterns (synchronous, diagonal, turning).
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Bayesian Optimization Framework
- Tunes CPG parameters (frequency, amplitude, coupling) for speed, energy efficiency, and cost of transport.
- Uses Gaussian Process (GP) surrogate models with exploration–exploitation balance.
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Simulation Environment
- MuJoCo + PyChrono interfaces for testing gait dynamics.
- Warm-up phase implementation for oscillator stabilization.
- Real-time logging of speed, displacement, energy, and cost of transport.
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Adaptive Terrain Response
- Integrates terrain classification with gait adaptation.
- Demonstrates robustness across sand, rock, and aquatic-like surfaces.