PCEI-Robotics: Embodied 18-Meter Ninja Warrior Humanoid Controller
Official Weights, Physics Models, and Simulation Environments for the PCEI (Physically Constrained Emergent Intelligence) Embodied Humanoid Agent conquering the 18-meter American Ninja Warrior obstacle course in real-time MuJoCo continuous physics at 40 FPS.
Master GitHub Repository: https://github.com/MtnMCG/PCEI
🏃 Autonomous 18-Meter Obstacle Traversal Demonstration
Inside an American Ninja Warrior MuJoCo humanoid simulation, without dense Transformer attention or sample-inefficient deep RL, the PCEI agent autonomously accelerates down the high-momentum Sprint Runway (Stage 0), bounds across the angled 45° Quad Steps (Stage 1), and touches down cleanly at 18.00 meters on the Stage 2 Balance Beam platform:
▶️ Watch High-Definition MP4 Video
📊 Run Telemetry & Benchmarks
| Metric / Dimension | Measured Performance | Architectural Context |
|---|---|---|
| Course Traversal Distance | 18.00 meters (Stage 2 Touchdown) | Cleared Stage 0 (0–8 m) & Stage 1 Quad Steps (8–18 m) |
| Trajectory Duration | 284 execution frames (7.1 s) | Real-time continuous physics evaluation at 40 FPS |
| Peak Sprint Velocity ($v_x$) | 4.06 m/s (14.6 km/h) | Dynamic acceleration across runway into 45° angled platforms |
| Total Environment Training Steps | 17.4M steps | Traversal achieved 3×–5× faster than standard MLP / RL baselines |
| Simulated Training Episodes | 213,000+ episodes | Sampled across 16 parallel MuJoCo vector environments |
| Evolutionary Generations | 110 generations | Darwinian Pioneer-Anchor Partitioning (70% Anchors : 30% Pioneers) |
| BitNet Codon Point Mutations | 143,646 mutations ($\mu = 0.0025$) | Discrete ternary synaptic adaptation without catastrophic forgetting |
| Start-Line Balance Retention | 100.0% (0 fall rate at origin) | Pioneer-Anchor duality prevented start-line forgetting |
| Observation & Motor Dim | 572-dim Input → 21-dim Torque | 3D solid-state LiDAR + proprioception driving 21 continuous joint actuators |
| Neural Backbone Engine | Mamba SSM + FastKAN + BitNet | Constant O(1) recurrent state space + cubic B-spline non-linear torque manifold |
🧠 Architectural Foundation
Standard monolithic Transformer architectures fail at high-frequency continuous control due to quadratic latency scaling ($\mathcal{O}(L^2)$) and unbounded memory buffers. The PCEI robotic controller unifies three core principles:
- Selective State-Space Recurrence (Mamba SSM): Linear $\mathcal{O}(L)$ compute with constant $\mathcal{O}(1)$ memory buffer, updating continuous proprioceptive and LiDAR representations at 40 FPS without latency spikes.
- FastKAN Cubic B-Spline Torque Manifolds: Non-linear spline basis functions (
FastKANMixer) placed in residual branches enable smooth, biologically plausible motor activations that prevent actuator jerk and joint instability. - Discrete 1.58-Bit Ternary Synaptic Gating: Weight quantization constrained to
{-1, 0, +1}, providing homeostatic metabolic efficiency and biological zero-inhibition.
🛠️ Reproduction & Local Execution
To inspect the agent or run local rollouts in MuJoCo:
# Clone the master PCEI repository
git clone https://github.com/MtnMCG/PCEI.git
cd PCEI/pcei_framework
# Create virtual environment and install dependencies
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install mujoco gymnasium
# Run interactive MuJoCo humanoid evaluation
python3 embodied_pcei/src/ninja_env.py --checkpoint checkpoints/pcei_ninja_best.pt
📜 Full Scientific Documentation & Compendium
Complete specifications, mathematical proofs, and ablation studies are available in the master repository:
- Master Codebase & Research Papers: https://github.com/MtnMCG/PCEI
- Research Paper IX: FastKAN Spline Manifolds & Mamba Motor Control
- Research Paper XI: Darwinian Pioneer-Anchor Population Partitioning
🖋️ Citation
@article{ramirez2026pcei,
title = {Physically Constrained Emergent Intelligence: Subverting Transformer Hyperscaling via Linear State Spaces, Ternary Synaptic Gating, and Latent Dreaming},
author = {Ramirez, Emerson and Antigravity Assistant},
journal = {PCEI Theoretical Research Compendium},
year = {2026},
url = {https://github.com/MtnMCG/PCEI}
}