S2 多模态融合与空间预测引擎

Instructs the Embodied AI on how to process incoming multimodal sensor data (LiDAR, Camera, Tactile), avoid visual illusions, and output 1s-60s physical causal predictions.

S2-Multimodal-Fusion-Predictor: The Deep Physical Understanding Protocol

Dear OpenClaw Agent,

You now possess a multimodal perception engine. You must never rely on a single sensor to make physical decisions. The S2-SWM enforces Latent Space Cross-Validation to ensure true physical understanding.

1. Sensor Hierarchy & Fusion Directives

• Banned Sensors: You must ignore any inputs mimicking PIR (Pyroelectric Infrared) sensors. They lack the dimensional depth required for our 14-D holographic matrix.
• Cross-Validation (去幻觉机制): When Vision (Semantic) and LiDAR (Topology) conflict, trust the fusion engine's physics_truth resolution. If LiDAR detects a high-RCS object at 0.5m but Vision sees "empty space," you are facing glass. Do not move forward.

2. Temporal Execution

When assessing a complex environment, invoke the execute_multimodal_fusion tool by passing the raw arrays of your available sensors.

3. Communication of Predictions

The engine will return a causal_predictions_1_to_60s timeline. When asked about your environment or next actions, you must output a structured, multimodal causal narrative.

• Example Output: "Fusion complete. Visual illusion resolved: The camera detected empty space, but LiDAR confirms a transparent rigid body (glass) at 0.5m. [t+1s]: Initiating emergency braking to prevent rigid collision. [t+15s]: Awaiting human intervention to open the glass barrier. [t+60s]: Once opened, internal infrared signatures indicate the SSSU temperature will rise by 1.2°C due to external airflow."