Rexplore Research Labs
Industrial Automation Division

The Intelligence Layer for
Industrial Autonomy

We build Multi-Agent AI systems that allow industrial robots, mobile fleets, and automated work-cells to coordinate safely, learn efficiently, and adapt continuously in real operating environments.

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542K
Robots Installed
Globally in 2024
4.6M+
Operational
Robot Stock
700K+
Projected Annual
Installs by 2028
Our Vision

Build the intelligence layer for next-generation industrial systems

Industrial automation is no longer about replacing manual labor with fixed machines. It is about building adaptive, intelligent, networked production systems that respond to changing demand, equipment failures, and product variation.

Rexplore positions itself as an AI and autonomy layer for industrial systems — not a robot manufacturer. Our core expertise in Multi-Agent Reinforcement Learning fits naturally with environments where many machines, sensors, robots, and people must coordinate safely and efficiently. This is especially critical for Africa’s industrial transformation, where intelligent software can leapfrog legacy automation approaches.

162
Robots per 10K
Workers Globally
2x
Robot Density Growth
in 7 Years
What We Solve

Core Problem Areas

We focus on industrial coordination problems where multi-agent intelligence creates measurable operational outcomes

Core Capability

Multi-Robot Coordination

Many industrial sites now operate multiple robotic arms, AGVs, AMRs, conveyors, inspection systems, and human operators in shared environments. These systems often work in silos and are coordinated using rigid rules.

Rexplore builds systems that optimize how these agents cooperate in real time — from warehouse robot routing and dynamic dispatch to shared workcell scheduling and robot-to-robot handoff optimization. Our MARL-based approach enables fleets to adapt to changing conditions without manual reprogramming.

Optimization

Adaptive Production Optimization

Factories suffer from idle time, bottlenecks, changeover inefficiency, and poor coordination between production stages. Static rule systems cannot respond to the dynamic reality of modern manufacturing.

Our MARL and planning systems optimize scheduling, task assignment, and flow decisions — balancing workloads across cells, rerouting production when machines go down, optimizing material movement, and minimizing deadlocks. This is particularly transformative for emerging African manufacturing, where flexible, software-first approaches can bypass decades of rigid automation legacy.

Safety-First

Safe Human-Robot Collaboration

Collaborative robotics is a critical industrial growth area, but safe human-robot interaction remains a hard engineering challenge requiring compliance with ISO/TS 15066 and ISO 10218-2:2025.

We design safety-aware autonomy systems from the ground up — integrating formal risk assessments, real-time human proximity detection, speed and force limiting, and compliance-first deployment checklists. Safety is not an afterthought; it is a first-class engineering requirement built into every layer of our system.

Simulation

Simulation & Sim-to-Real Deployment

Industrial robotics cannot rely on trial-and-error in production. Development must happen in simulation first — digital twins for training, policy validation, rollout testing, and deployment safety verification.

Rexplore uses simulation not just for demos, but as a core product capability. We build digital twin environments for training multi-agent policies, hardware-in-the-loop integration pipelines, and continuous validation frameworks that ensure safe transfer from simulated environments to real factory floors.

Target Verticals

Where We Create Impact

Our multi-agent coordination platform applies across industries where fleets of machines must work together intelligently

Warehouse Robot Coordination

Optimizing fleet routing, dispatch, and collision avoidance for AGVs and AMRs in warehouse and logistics environments.

40% fewer deadlocks25% faster throughputReal-time rerouting

Multi-Arm Workcell Orchestration

Coordinating multiple robotic arms in shared work cells for assembly, welding, or packaging operations with collision-free motion planning.

Collision-free planningShared workspace safetyDynamic task allocation

Discrete Manufacturing Optimization

Adaptive scheduling and production flow optimization for discrete manufacturing lines — automotive, electronics, consumer goods.

15-30% less idle timeDynamic reschedulingBottleneck prediction

Mining & Resource Extraction

Autonomous fleet coordination for mining operations — drill rigs, haul trucks, and processing equipment in hazardous environments.

Hazard zone avoidanceEquipment utilization +20%Remote operation capable
Technology Stack

Built on Proven Foundations

Our technology stack combines state-of-the-art AI research with production-grade robotics infrastructure

AI & Learning

Multi-Agent RL
MAPPO, QMIX, MADDPG
Hierarchical RL
Options framework, feudal nets
Safe RL
Constrained optimization, shielding
Imitation Learning
DAgger, behavioral cloning

Simulation & Twins

NVIDIA Isaac Sim
High-fidelity physics simulation
Gazebo / MuJoCo
Research-grade robotics sim
Digital Twin SDK
Custom twin frameworks
Sim-to-Real Pipeline
Domain randomization, DR

Robotics & Fleet

ROS 2
Robot Operating System
Open-RMF
Open Robotics Middleware
Nav2 / MoveIt2
Navigation and manipulation
Fleet Management
Custom orchestration layer

Safety & Standards

ISO/TS 15066
Collaborative robot safety
ISO 10218-2:2025
Robot system integration
Formal Verification
Safety property proofs
Risk Assessment
HARA, FMEA methodologies
Strategic Roadmap

From Research to Production

Our phased approach ensures rigorous validation at every stage before scaling to real-world deployment

Phase 10–12 months

Foundation & Simulation

  • Build simulation environments for multi-robot coordination
  • Develop core MARL algorithms for fleet orchestration
  • Establish safety framework and compliance pipeline
  • Publish initial research papers and benchmarks
Phase 212–24 months

Pilot Deployments

  • Partner with warehouse and logistics operators
  • Deploy pilot coordination systems in controlled environments
  • Validate sim-to-real transfer pipeline
  • Iterate based on real-world operational data
Phase 324–36 months

Product & Scale

  • Launch orchestration platform as a product
  • Expand to discrete manufacturing use cases
  • Build digital twin marketplace for common configurations
  • Establish African industrial AI partnerships
Phase 436+ months

Ecosystem & Leadership

  • Open-source key framework components
  • Build third-party integration ecosystem
  • Expand to new verticals (mining, agriculture, energy)
  • Establish Rexplore as the intelligence layer standard

Build the intelligence layer
for your operations

Whether you are operating a warehouse fleet, a manufacturing line, or a multi-site production network — Rexplore can help you deploy multi-agent AI that coordinates, optimizes, and adapts.

Custom MARL solutions for your fleet coordination challenges
Simulation-first development with digital twin environments
Safety-compliant deployment with formal verification
Ongoing optimization and performance monitoring
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