Applied research on identity, AI software provenance, predictive operations and cybersecurity for humanoid and autonomous robots. The focus is the control layer around embodied AI: how machines are identified, authorized, observed and held accountable.
Three foundational research tracks define the initial Solarion Robotics research agenda.
Persistent identity becomes more important—not less—when an AI system can move through facilities, change software, interact with people and invoke enterprise tools. This note proposes a machine-identity model that binds a robot, its accountable sponsor, its runtime software and its authorization state.
Robot fleets produce continuous signals about temperature, vibration, battery health, joint errors, network quality, latency and anomalies. This note describes how those signals can become explainable risk indicators and maintenance-priority forecasts rather than another passive dashboard.
A robot may contain models, agents, perception pipelines, middleware, drivers and vendor firmware. This note proposes a desired-state and provenance architecture that can prove which software was approved, what artifact was deployed and whether the robot still reports a matching runtime state.
Solarion Robotics research notes describe proposed architectures, engineering hypotheses and product research directions. Unless specifically stated otherwise, they are not peer-reviewed standards, safety certifications or claims of independent validation.