The "last 40% accuracy costs 10x the hardware" observation is the load-bearing truth of this architecture. Annie's nav stack at 60% goal-finding accuracy needs: one Pi 5 ($80), one lidar ($35), one USB camera ($25). Total hardware: under $150. Annie's nav stack at 90% goal-finding accuracy needs: all of the above, plus a Panda Orange Pi 5 Plus with 8GB VRAM ($200), a reliable 5GHz WiFi channel (dedicated AP, $40), and a 4-tier software architecture spanning three machines. The marginal 30 percentage points of accuracy cost roughly 2.5× the total hardware budget and all of the distributed-system complexity. That tradeoff is not obviously worth making for a home robot whose worst-case failure mode is "turn around and try again."

There is a relaxation pattern even cheaper than "buy a smaller model" — call it dormant-hardware activation. Before any new purchase, Annie's owner already has three idle compute tiers that the original architecture did not count: (1) the Hailo-8 AI HAT+ on Pi 5 — 26 TOPS, sitting idle for navigation today, capable of YOLOv8n at 430 FPS with sub-10ms latency and zero WiFi dependency; (2) Beast, a second DGX Spark with 128 GB unified memory, always-on but workload-idle since session 449; and (3) an Orin NX 16GB module at 100 TOPS Ampere, already owned and reserved for a future Orin-native robot chassis. This changes the constraint math. The VRAM ceiling that forced Gemma 4 E2B to juggle four jobs, the WiFi cliff-edge that made safety feel fragile, the compute budget that capped multi-model pipelines — all become negotiable without buying anything. This is zero-capex relaxation: unlike spending $250 on an Orin NX or $500 on a bigger GPU, activating hardware you already own costs only engineering time.

Three constraints are relaxable today, for under $200 combined, with immediate effect on reliability. First: speed. Dropping from 1 m/s to 0.3 m/s costs nothing and eliminates the two most documented failure modes in the session logs — turn overshoot (640% at speed 30) and WiFi-induced positional drift (10cm per 100ms spike). The nav physics simply become forgiving at low speed. Second: accuracy target. Accepting 60% first-try accuracy with a retry loop produces ~85% task success — within 5 points of the current 90% target — at zero hardware cost, no Panda required. Third: WiFi to USB tether. An $8 cable eliminates the cliff edge that Lens 04 identified as the single highest-risk parameter in the entire system, at the cost of a 2m tether that a retractable cable reel can absorb.

The constraint the user does not actually care about is SLAM accuracy. The Phase 1 and Phase 2 research treats SLAM map fidelity as a foundational requirement — accurate localization enables semantic map annotation, loop closure, and goal-relative path planning. But for Annie's actual use cases (fetch charger, return to dock, avoid Mom), the robot does not need to know it is at coordinate (2.3m, 1.1m) in a globally consistent map. It needs to know: is the goal in frame? Is something blocking forward motion? Have I been here before? All three questions are answerable with the VLM alone, without a SLAM map, to 60–70% accuracy. The SLAM investment buys the remaining 20–30 points of spatial consistency at the cost of 3 additional services (rf2o, EKF, slam_toolbox) and a Docker container that has required 5 dedicated debugging sessions to stabilize.

Hardware trends will relax the VRAM constraint within 18–24 months — but dormant-hardware activation collapses that timeline to weeks. The binding constraint for running VLM + SigLIP simultaneously is the 8GB VRAM ceiling on Panda's Mali GPU. The Jetson Orin NX 16GB (already owned, reserved for the future robot chassis) doubles that ceiling at $0 incremental cost the day it is activated. Beast's 128 GB unified memory can host any specialist model the pipeline needs without touching Panda's budget at all. And Hailo-8 carries the safety layer off-GPU entirely — no VRAM required. The "VRAM per model" curve is following the same trajectory as CPU megahertz in the 1990s: what requires dedicated hardware today will be a background service tomorrow. But Annie's household doesn't have to wait for 2027 — the dormant compute is already on-site.

The most architecturally disruptive relaxation is right-sizing the model to the task. Every "LEFT MEDIUM" command passes through Gemma 4 E2B's full autoregressive stack — a step that pays for reasoning capacity on a task (detection) that doesn't need it. Open-vocabulary detectors close this gap directly: NanoOWL at 102 FPS handles simple noun goals ("kitchen", "door", "person"); GroundingDINO 1.5 Edge at 75 FPS with 36.2 AP zero-shot handles richer prompts. Both fit TensorRT on Panda in a fraction of Gemma's 3.2 GB. Route goal-finding and scene classification to them; keep Gemma resident for questions that genuinely require language ("is the glass door closed?" "is Mom in the room?"). The VLM stops being the critical path for every frame and becomes the slow deliberative layer — the System 2 of a proper dual-process stack. And with the Hailo-8 added as L1 safety, the architecture finally matches the IROS dual-process result (66% latency reduction, 67.5% vs 5.83% success) without a single new hardware purchase. (Cross-ref Lens 06 on reliability layering, Lens 13 on right-sized models.)