EBuddy: a workflow orchestrator for industrial human-machine collaboration
It addresses the problem of inconsistent and inefficient human-machine collaboration in industrial environments, offering a domain-specific solution.
The paper tackles the bottleneck of scaling expert know-how in industrial workflows by introducing EBuddy, a voice-guided orchestrator that operationalizes expert practice as a finite state machine, resulting in substantial reductions in end-to-end process duration for tasks like impeller blade inspection and repair preparation.
This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets a recurrent bottleneck in tool-intensive workflows: expert know-how is effective but difficult to scale, and execution quality degrades when procedures are reconstructed ad hoc across operators and sessions. EBuddy operationalizes expert practice as a finite state machine (FSM) driven application that provides an interpretable decision frame at runtime (current state and admissible actions), so that spoken requests are interpreted within state-grounded constraints, while the system executes and monitors the corresponding tool interactions. Through modular workflow artifacts, EBuddy coordinates heterogeneous resources, including GUI-driven software and a collaborative robot, leveraging fully voice-based interaction through automatic speech recognition and intent understanding. An industrial pilot on impeller blade inspection and repair preparation for directed energy deposition (DED), realized by human-robot collaboration, shows substantial reductions in end-to-end process duration across onboarding, 3D scanning and processing, and repair program generation, while preserving repeatability and low operator burden.