US 12,511,598 B1

A computer-implemented artificial intelligence (AI) system for autonomously managing a vehicle service center using a fleet of humanoid robots, comprising: a centralized AI control module (102), a fleet sensing module (106), and a robotic actuation module (308) configured to:

a.receive and analyze multi-modal sensor data (304) and vehicle diagnostics (204), including specialized diagnostics for Electric Vehicles (EVs) and Software-Defined Vehicles (SDVs) from model year 2005 onwards;

b.identify vehicle issues and generate explainable AI (XAI)-based repair strategies (306a, 306b);

c.generate and dispatch a service plan (206) to humanoid robots (104) with dynamically assigned roles (306c);

d.adapt control commands (308) based on real-time environmental feedback (306e);

e.update and manage a digital twin of a vehicle (118) and service center (100);

f.constrain humanoid robot operation to user-defined geospatial boundaries (3061, 612);

g.provide customer updates (218) via humanoid robots (104) using emotion and sentiment recognition (306k) algorithms;

h.detect and respond to security threats (306j), including autonomously detecting and responding to malfunctions or offline states of individual humanoid robots within said fleet by re-allocating tasks and dispatching assisting robots for recovery or removal operations;

i.learn humanoid robotic behaviors using synthetic training data (306d) and reinforcement learning (306c); and

j.optimize asset utilization by dynamically managing external vehicle staging areas (606) and internal service bays (118) to maximize throughput and revenue generation from the service center property, including systematically identifying, triaging, and completing previously unfinished vehicle service projects.

The system of claim 1, wherein humanoid robot roles include: customer check-in (202), payment (218), diagnostics (204), physical repair (208), logistics (210), outdoor maintenance (212, 600), and security monitoring (306j).

The system of claim 1, wherein the humanoid robots (104) are equipped with multi-degree-of-freedom hands (502) containing force sensors (502a) and tactile sensors (502b) for fine manipulation and human-safe interaction, including handling high-voltage EV components with specialized safety protocols.

The system of claim 1, wherein humanoid robot control includes autonomous selection and operation of interchangeable end-effectors (510) from a centralized automated tool magazine (112), with said end-effectors capable of self-diagnosis for wear and calibration drift.

The system of claim 1, further comprising a user interface (108, 310) configured to: monitor humanoid robot operations, assign priority tasks, override behavior, review logs, and configure geospatial boundaries using augmented reality overlays.

The system of claim 1, wherein the AI control module (102) includes a cyber-physical security framework with: AES-256 encryption, role-based access control (RBAC), immutable audit logs, and blockchain-based supply chain integration.

The system of claim 1, wherein emotion and sentiment recognition is performed via natural language processing, facial micro-expression analysis, and vocal tone analysis.

A computer-implemented method for autonomously operating a vehicle service center with humanoid robots, comprising:

a.receiving a customer request (202) and scanning a vehicle with multi-modal sensors (204), including specialized scans for Electric Vehicle (EV) battery health and thermal management;

b.generating a digital twin of a vehicle and service center facility;

c.identifying service issues using explainable AI (XAI) reasoning (204);

d.creating and dispatching a multi-task service plan (206) constrained by geospatial boundaries (612);

e.coordinating a humanoid robot fleet (104) to perform repair (208, 408), logistics (210, 410), and customer interaction tasks (202, 402), said coordination including autonomously detecting and responding to malfunctions or offline states of individual humanoid robots within the humanoid robot fleet by re-allocating tasks and dispatching assisting humanoid robots for recovery or removal operations;

f.generating adaptive control commands in real time using sensor feedback;

g.conducting quality control (214) and updating machine learning models;

h.providing customer updates (218) using emotion-aware humanoid interactions;

i.logging all operational data;

j.detecting and responding to security events autonomously;

k.autonomously managing and re-organizing external vehicle storage and staging areas (606) to enhance operational flow and maintain property aesthetics through upkeep of customer-facing areas; and

l.systematically identifying, triaging, and completing previously unfinished vehicle service projects.

The method of claim 8, wherein the humanoid robots (104) autonomously drive a vehicle into a service bay (118) or guide it onto an automated lift system, and can connect/disconnect EV charging cables and manage charge scheduling, including dynamic load balancing based on grid demand and electricity pricing.

The method of claim 8, wherein the humanoid robots (104) use semantic understanding to interpret and manipulate objects in the environment.

The method of claim 8, wherein the humanoid robots (104) perform real-time natural language conversations with customers (120), adapting tone and gestures based on detected sentiment.

The method of claim 8, wherein vehicle status updates (218) are visually displayed on humanoid robot interfaces (504d), including annotated diagnostic visuals and live progress bars.

The method of claim 8, further comprising automated key intake, storage, and retrieval via secure compartments (110).

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a system to:

a.receive service requests and sensor data, including data from connected vehicle streams via vehicle-to-x (V2X) communications for predictive maintenance;

b.identify vehicle faults and generate explainable AI (XAI)-based repair strategies, including for EV powertrains and high-voltage systems;

c.create and dispatch a task plan to humanoid robots with adaptive command generation;

d.monitor humanoid robot performance and environment feedback in real time, including detecting malfunctions or offline states of individual humanoid robots and initiating autonomous response protocols for task re-allocation and peer-to-peer assistance;

e.constrain all movement to user-defined geospatial boundaries;

f.manage emotion-aware customer interactions via humanoid agents;

g.update a digital twin of a vehicle and a service center for simulation, monitoring, and predictive maintenance;

h.log all operations and performance data; and

i.detect and respond to unauthorized access or anomalies, including cyber-physical attacks aimed at compromising humanoid robot actions or vehicle networks.

The computer-readable medium of claim 14, wherein reinforcement learning is applied to humanoid robot training for dynamic physical manipulation in vehicle repair scenarios.

The computer-readable medium of claim 14, further comprising integration with a synthetic data engine (306d) that simulates diverse vehicles, components, lighting, and damage types for training, including EV-specific components and charging scenarios, and continually incorporates human overrides and corrections to improve autonomous decision-making.

The computer-readable medium of claim 14, wherein user interaction history and customer preferences are used to adapt humanoid robot behavior in future sessions.

The computer-readable medium of claim 14, wherein humanoid robot navigation (306g) includes obstacle detection and path planning using LiDAR, stereo vision, and semantic map overlays.

The computer-readable medium of claim 14, wherein system logs are periodically hashed and stored on a blockchain to enable verifiable audit trails for compliance and trust.

The computer-readable medium of claim 14, wherein the instructions further cause the system to perform coordination of multi-humanoid robot collaboration (306c, 400), including joint manipulation tasks, human-robot cooperation, and augmented reality-assisted human oversight (108, 310, 428).