Abstract
A robot navigates a 100×100 continuous grid over 800 steps across 5 distinct phases of increasing complexity: mobile hazard barriers, relocating resources, and variable sensor noise. The robot has no global map, no reward function, and no policy updates — strategy emerges from genome-encoded survival pressure and local sensing only. Mean fitness (harsh_dynamic): 137.53. Best seed (702): 139.27. Adaptation score: 0.744 across all 5 phases.
1. Experimental Setup
Phase Structure
| Phase | Steps | Primary Challenge | Hazard Type |
|---|---|---|---|
| Phase 1 | 0–160 | Baseline navigation | Static hazard zones |
| Phase 2 | 160–320 | Relocating resources | Mobile food zones + static hazards |
| Phase 3 | 320–480 | Hazard barriers | Dynamic barriers (maximum pressure) |
| Phase 4 | 480–640 | Sensor noise | Gaussian noise on all sensory inputs |
| Phase 5 | 640–800 | Combined challenge | All previous elements simultaneously |
- Local sensory range only — no global state observable
- Genome-encoded: risk aversion, exploration radius, memory weight, jump probability
- No explicit map construction or planning algorithm
- Energy-based survival: collecting resources increases energy; hazards deplete it
2. Results
Phase-by-Phase Performance
| Phase | Challenge | Approx. Mean Survival | Notes |
|---|---|---|---|
| Phase 1 | Baseline | >95% of steps | Easy baseline |
| Phase 2 | Mobile resources | ~88% | Requires active tracking |
| Phase 3 | Hazard barriers | ~72% | Maximum selection pressure |
| Phase 4 | Sensor noise | ~80% | Memory weight selected higher |
| Phase 5 | Combined | ~69% | Hardest condition |
| Overall | — | Stabilizes ~step 550 | Adaptation score: 0.744 |
3. Key Findings
- Mean fitness 137.53 in harsh_dynamic condition — robust multi-phase adaptation.
- Phase 3 produces the strongest genome selection pressure; organisms with lower risk tolerance survive.
- Stability at ~550 steps: genome distribution converges to a stable behavioral profile before the final phase.
- Population reaches 1,200: positive selection maintained even under maximum environmental pressure.
- Adaptation score 0.744: robot maintains 74.4% of Phase 1 performance across all 5 phases — strong for no-map navigation.
4. What This Demonstrates
Emergent Navigation Without Explicit Planning
inZORi produces adaptive navigation strategies in complex, multi-phase environments without any map, planning algorithm, or reward shaping. The 5-phase structure specifically tests transfer: does a strategy that works in Phase 1 survive Phase 3? The genome distribution shifts at each phase, selecting different behavioral profiles — genuine adaptation, not pre-programming.
Analogs: Warehouse robots, UAVs in variable wind, autonomous vehicles in changing traffic — any system needing adaptation without retraining.
5. Reproducibility
Framework: inZORi v1.0 | Grid: 100×100 | Steps: 800 | Phases: 5
Fitness: Weighted combination of survival steps, resources collected, hazard hits avoided
Best seed: 702 (fitness 139.27) | Max population: 1,200
Note: Genome parameters and selection mechanics are proprietary. Environment layout, phase structure, and evaluation metrics are fully disclosed.