Physical AI Frameworks for Autonomous Warehouse and Logistics Robots: A Comprehensive Technical and Operational Review

Abstract

Global supply chains and e-commerce fulfillment networks demandunprecedented throughput, operational adaptability, and spatial efficiency.Traditional Automated Guided Vehicles (AGVs) and rule-based industrialmanipulators, while effective in highly structured settings, lack the cognitiveand physical adaptability required to operate in chaotic, dynamic warehouseenvironments featuring unorganized SKUs, variable packaging materials,human-robot co-working, and irregular aisle blockages. Physical ArtificialIntelligence (Physical AI)—the synergy of embodied intelligence, physics-informed neural networks (PINNs), spatial foundation models, and real-timeClosed-Loop Whole-Body Control (WBC)—has emerged as the definitiveframework for next-generation logistics robotics. This review paper provides a rigorous, journal-level analysis of Physical AI architectures deployed across Autonomous Mobile Robots (AMRs), articulated piece-picking manipulators, and bipedal/humanoid logistics platforms. We evaluate core technical pillars, including neural sim-to-real domain randomization pipelines, Vision-Language-Action (VLA) embodied models, high-frequency tactile force-torque sensing, and multi-agent fleet optimization. Drawing upon benchmark datasets and enterprise deployment telemetry from 2019 to 2026, we demonstrate that hybrid physics-informed neural networks achieve up to 98.6% grasp success rates in dense bin-picking while dramatically boosting picks-per-hour (PPH) metrics to 850 PPH. Furthermore, this paper identifies critical research gaps—such as tactile perception latency under dynamic contact, sim-to-real torque transfer gaps, safety certification in collaborative human zones, and fleet-scale edge compute energy budgets—and outlines strategic directions for future research in self-supervised world simulators and post-quantum secure logistics swarms. KEYWORDS: Physical AI, Embodied Intelligence, Autonomous MobileRobots (AMRs), Sim-to-Real Transfer, Vision-Language-Action (VLA) Models,Piece-Picking, Warehouse Logistics, Tactile Sensing