| Imagine a delivery robot navigating a sidewalk: Its wheel encoder shows it's slowing down unexpectedly
Its thermal sensor detects a warm object ahead
Its motion prediction model predicts the wheels should still be moving normally. What if the robot’s system structures this into real-time schemas like: { "sensor_type": "rotary_encoder",
"metrics": {"wheel_speed_m_s": 0.1},
"recent_values": [0.8, 0.7, 0.1],
"metadata": {
"units": "m/s",
"mount_position": "rear_left_wheel" } } A similar schema from the thermal sensor, showing a 37°C object 0.6 m ahead
Another from the prediction model, showing expected speed of 0.8 m/s The LLM than receives these and reasons: “Observed wheel speed dropped sharply from 0.8 to 0.1 m/s.
Thermal sensor shows a warm object directly ahead.
Predicted speed was 0.8 m/s — but reality diverged.
Likely obstruction from human or animal.
Initiate 5-second pause and begin visual recording.” I'm thinking of building a lightweight library to make this plug-and-play: You feed it raw, filtered, or predicted sensor data, and it outputs structured, LLM-readable snapshots — ready for reasoning and decision-making. Not launched yet — just validating. |