Abstract: In large-scale heterogeneous robot systems (HRS), scheduling efficiency in terms of throughput and makespan relies on exploiting parallel execution, while human-issued safety and precedence instructions impose rigid temporal-logic constraints that create severe combinatorial complexity and challenge traditional optimization and metaheuristic methods. We propose Asynchronous Logic-Induced Sleep-Wake Coordination (ALIS-WC), an event-driven reinforcement learning (RL) framework that performs sleep-wake scheduling of heterogeneous robots under Linear Temporal Logic (LTL) constraints. At its core, ALIS-WC applies a two-stage filtering mechanism: it first filters out robots that are physically incapable of contributing to the current decision, and then uses temporal-logic dependencies to label the remaining robots as sleeping or eligible, so that the learned dispatch policy only coordinates among idle, constraint-compliant robots at each decision event. We first design a human-ALIS-WC interaction interface: a lightweight language front-end that maps natural-language (NL) mission descriptions into LTL safety and precedence clauses for the scheduler using a compact 8B translator that achieves high NL-to-LTL accuracy. We further design a potential-based reward-shaping term over normalized global mission progress that provides a difficulty-invariant learning signal and accelerates policy learning without changing the underlying objective. Experiments on large-scale graph-based benchmarks with dozens of heterogeneous robots, 100 tasks, and up to 10 LTL clauses show that ALIS-WC improves makespan and task completion over strong metaheuristic, search-based, and reinforcement-learning baselines while maintaining 100% satisfaction of all enforced temporal-logic constraints. Additional stress tests with up to 60 clauses indicate that ALIS-WC remains effective under much denser temporal-logic specifications.