The framework allows high-level decisions to persist over longer time scales, while low-level loops run at high frequencies (e.g., 100 Hz for motor control vs. 1 Hz for path planning).
At this apex, the system uses symbolic reasoning, planning, or reinforcement learning policies. The high-level controller interprets the abstracted state, evaluates goals (e.g., "avoid obstacle," "maximize energy efficiency"), and issues adaptive commands. l2hforadaptivity
In reality, data is rarely that clean.
The system learns which low-level features are relevant for high-level tasks. Irrelevant variations (e.g., lighting changes in a robot’s camera) are filtered out, while critical changes (e.g., a sudden drop in floor traction) are propagated upward. The framework allows high-level decisions to persist over
Different applications and use cases have varying requirements in terms of scalability, security, and decentralization. For instance, DeFi applications may require high throughput and low latency, while NFT marketplaces may prioritize decentralization and security. L2H for adaptivity addresses this need by providing a flexible and modular framework that allows developers to tailor their L2 scaling solutions to specific use cases. Irrelevant variations (e