arXiv:2606.23978v1 Announce Type: cross Abstract: We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment. SLAM (Scan/Label/Apply/Manifest) throughput directly influences system congestion and operational efficiency. Our RL-based control approach dynamically recommends SLAM throughput settings that adaptively balance throughput maximization with downstream stability through intelligent adjustment of throttling behavior. We include a history-informed state representation, action space abstraction for delayed-impac
The increasing complexity and scale of automated warehouse operations necessitate more sophisticated control mechanisms, making advanced AI solutions like offline RL immediately relevant.
Optimizing warehouse logistics through AI directly impacts supply chain efficiency and cost, a critical factor for global commerce and a leading indicator of AI's broader commercial deployment.
Traditional warehouse management systems will evolve to incorporate more dynamic, adaptive AI-driven control, moving beyond static optimization to real-time, learning-based adjustments.
- · Logistics and e-commerce companies
- · AI software providers
- · Robotics manufacturers
- · Warehouse automation sector
- · Companies with manual or legacy warehouse systems
- · Inefficient logistics providers
Increased efficiency and reduced operational costs in automated warehouses.
Faster and more reliable fulfillment, leading to better customer satisfaction and reduced inventory holding times.
Broader adoption of AI-driven control systems across various industrial automation sectors, accelerating the development of more general purpose AI agents in physical environments.
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Read at arXiv cs.AI