KAYAP represents the next evolution in the NDM (Neural Differential Manifold) robotics suite. While earlier NDM iterations focused on raw adaptability through continuous weight evolution, KAYAP introduces a specialized Hardened Elastic Manifold strategy.
Its goal is not just adaptation — but guaranteed survivability in physically chaotic and failure-prone environments.
🧩 Core Innovation: The Elastic Manifold Strategy
Traditional neural controllers attempt to predict absolute thrust values, which makes them fragile under noise or partial hardware failure.
KAYAP instead operates on an Elastic Manifold:
🔹 Delta-Based Control
- The AI predicts a ± delta relative to a stable hover value
- It never outputs raw motor power directly
🔹 “Death Spiral” Prevention
- Weight updates are constrained to an elastic range
- This prevents runaway weight migration that causes drones to flip uncontrollably
- Sensor noise or sudden motor loss no longer leads to catastrophic instability
This design directly addresses the classic neural controller death spiral problem.
🧠 Training Architecture: Learning from a Teacher
KAYAP uses an Imitation → Autonomy pipeline to harden the manifold before full independence.
1️⃣ The PD Teacher Phase
- For the first 180 episodes, a classical Proportional–Derivative (PD) controller acts as a teacher
- The NDM observes correction signals and maps them into its internal manifold geometry
2️⃣ Teacher Decay
- The teacher’s influence is gradually reduced
- The NDM must rely entirely on its learned internal “reflexes”
3️⃣ Mirror Training
- Every training step is mathematically mirrored
- Learning to recover from a left-leaning gust automatically grants recovery from a right-leaning one
- This enforces geometric symmetry inside the manifold
🧪 The “Hardened” Test Suite
KAYAP is evaluated using a four-stage stress gauntlet designed to break standard neural controllers.
| Test | Challenge | Environmental Stress | Hardware Condition |
|---|---|---|---|
| 1 | Baseline | Standard Hover | 100% Efficiency |
| 2 | Heavy Wind | Lateral Force -4.0 (Left) | 100% Efficiency |
| 3 | Underpowered | Low Voltage +3.0 (Right) | 85% Motor Capacity |
| 4 | Extreme (Boss) | Catastrophic Failure -6.0 | 75% Power (Crippled) |
📊 Performance Analysis: Run 4 (The “Success Case”)
The repository highlights Run 4 as the benchmark for a truly Hardened Manifold.
- Runs 2 & 3 ended in Total System Collapse (falling out of the simulation or extreme rotational divergence)
🟢 Run 4 Results
-
Priority Learning
- The AI stabilized rotation first, even before reaching the target altitude
- Final Roll: 0.008 rad under crippled motor conditions
-
Weight Stability
- High Momentum: 0.98
- Low Learning Rate: 0.0005
- Internal manifold geometry remained stable under extreme physical stress
Training Data as a Structural Prior
Neural Differential Manifolds (NDM) do not merely learn control outputs; they learn a geometry of response.
When trained on balanced, extreme, and mirrored trajectories, the manifold hardens into a stable elastic structure capable of surviving disturbances, asymmetries, and partial system failures.
Conversely, poor, narrow, or biased training data produces a fragile manifold geometry, leading to runaway dynamics and catastrophic failure under stress.
🔑 Key Takeaway
KAYAP proves that robotic stability is not about faster reactions.
It is about geometrically hardening the learning manifold itself.
A well-trained Neural Differential Manifold does not blindly chase goals —
it prioritizes its own structural survival.
🔗 Repository
📦 Source Code & Experiments:
https://github.com/hejhdiss/ndm-applications-robotics
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