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 , from warehouse fleets to multi-arm workcells, discrete manufacturing lines, and resource extraction.
Autonomous fleet coordination, drill rigs, haul trucks, and processing equipment in hazardous environments. One of four deployment domains the division is building toward.
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, hand-written 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. Our MARL-based approach lets fleets adapt to changing conditions without manual reprogramming.
Warehouse fleet routing and dynamic dispatch (AGVs, AMRs).
Multi-arm workcell orchestration with collision-free motion planning.
Adaptive production scheduling across discrete manufacturing lines.
We design safety-aware autonomy from the ground up, formal risk assessments, real-time human proximity detection, speed and force limiting, compliance-first deployment checklists.
ISO/TS 15066 · ISO 10218-2:2025Industrial robotics cannot rely on trial-and-error in production. We build digital-twin training environments, hardware-in-the-loop pipelines, and continuous validation that ensures safe transfer from simulation to factory floor.
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 balance workloads across cells, reroute production when machines go down, and minimise deadlocks, encoded below as a safety constraint over an agent cluster.
// Safety Constraint Definition @invariant forall agent_a, agent_b in Cluster { distance(agent_a.pos, agent_b.pos) >= MIN_SAFETY_MARGIN; velocity_alignment(agent_a, agent_b) < THRESHOLD; } fn resolve_conflict(a1: Agent, a2: Agent) -> Trajectory { // Compute optimal detour in latent space return LatentManifold.solve(a1, a2); }
Above: a simplified pseudocode sketch of how a coordination policy checks pairwise safety before resolving a trajectory conflict , adapted from the kind of constraint solver our research team prototypes in simulation.