Camera has zero knowledge around corners, behind furniture, or above its own plane. Every visual navigation system is imprisoned by this: the robot can only see what the camera sees, and the camera sees only what the photons reach. No algorithm changes this.

At speed 30, a 5° IMU turn target yields 37° of actual rotation. Motor torque releases kinetic energy into the chassis — it continues turning after the signal stops. The overshoot is not a software bug. You cannot wish it away with a tighter control loop; you can only predict and pre-brake.

The RPLIDAR C1 sweeps a single horizontal disc at chassis height (~130mm). Table edges, hanging cords, open dishwasher doors, and chair rungs above 130mm are invisible to it. Glass doors reflect IR and return as walls or as nothing. These are not edge cases — they are the majority of real home obstacles.

Annie's inference runs on Panda (18ms per frame). But the round-trip across household WiFi — Pi sends JPEG, Panda returns command string — adds 30–80ms under load, with occasional 150–300ms spikes. At 1 m/s, a 300ms spike means the robot has moved 30cm with no steering correction. The VLM's 58 Hz frame rate is a local measurement; the effective command rate, network-inclusive, is 10–20 Hz on a good day.

The Gemma 4 E2B ViT (150M params) uses ~14ms for vision encoding and ~4ms for text decoding. A second model on Panda (e.g., SigLIP 2 at 800MB) competes for VRAM and thermal budget. There is one camera. You cannot run 6 VLM instances in parallel on 6 different image streams — you must time-slice a single stream.

TurboPi omits rotary encoders entirely. Dead-reckoning from motor commands is unusable (wheel slip, surface variation). This forced rf2o lidar odometry as the primary odometry source — which turned out to be more accurate in practice. A constraint that looked like a hardware deficiency produced a better architecture than the "standard" approach.

The AI HAT+ ships a Hailo-8 accelerator (26 TOPS) physically attached to the same Pi that carries the camera. It is currently unused by the nav stack. YOLOv8n runs at 430 FPS locally on this chip with <10ms latency and zero WiFi dependence. The assumption that "inference must be remote on Panda" was never a physics constraint — it was a first-pass implementation decision made before the Hailo was on the bill of materials. The hardware to run a fast local safety tier has been sitting idle the whole time.

Without revisiting previously mapped areas, trajectory error accumulates as a random walk. Scan-matching gives relative accuracy (frame-to-frame) but long-range absolute pose error grows unboundedly in linear environments like hallways. This means Annie's SLAM is accurate for short exploratory runs but will drift in large, featureless rooms. Visual loop closure (AnyLoc, SigLIP embeddings) addresses this — but at added VRAM cost on an already-constrained Panda.

The current nav command schema ("LEFT MEDIUM", "CENTER LARGE") maps a continuous visual field onto 9 discrete cells. This is an algorithmic choice, not a physics constraint — the ViT encoder produces 280-dimensional continuous feature vectors per image. Discretizing to text sacrifices geometric precision in exchange for human readability and easy downstream parsing. The 9-cell schema is a convention that could be replaced entirely by feeding raw embeddings to a learned steering function.

All current systems, including Annie's Phase 1 nav loop, send one question to the VLM per frame. This emerged naturally from single-task systems where one question was all you needed. At 58 Hz, the assumption is gratuitous waste: alternating four different queries across frames gives each task 14–15 Hz — faster than Waymo's planning loop. The research shows this is a one-line code change (cycle_count % N dispatch). This convention costs nothing to break.

The 4ms text-decoding step is not technically necessary for place recognition or scene-change detection. The SigLIP ViT encoder output — 280 tokens of high-dimensional embedding — IS the scene representation. Cosine similarity on these vectors finds visually similar locations without any language at all. Text output is a convention inherited from chatbot pipelines, not a requirement of visual intelligence. Annie's own ArUco homing is the existence proof: cv2.aruco.ArucoDetector + solvePnP(SOLVEPNP_ITERATIVE) returns a 6-DoF pose in ~78 µs per call on the Pi ARM CPU — no text, no VLM, no network, and accurate to ~1.7 cm. The "useful output is a text string" convention is already broken inside the codebase; we just haven't generalized it.

VLMaps, the research reference for Phase 2c, reframes the map entirely: the occupancy grid is not a navigation substrate, it is a semantic memory surface. Navigation is a secondary benefit. Once you annotate SLAM grid cells with VLM scene labels over time, you have built a queryable model of the home's layout — rooms, furniture positions, traffic patterns. The map becomes the knowledge base Annie consults to answer "where is Mom usually in the morning?"