Questions we're working on
Cogninoid Labs pursues research at the frontier of physical AI — where machines must not just reason, but act, sense, and adapt in the real world.
Research Areas
Embodied AI & Learning
Studying how AI systems can learn from physical interaction with the world — through reward signals, demonstrations, and closed-loop feedback rather than purely offline data.
Robotic System Design
Designing robot hardware and software architectures that are modular, reusable, and amenable to AI-driven control across a range of manipulation and locomotion tasks.
Agentic AI Systems
Building AI agents that can plan, write code, call tools, and execute multi-step scientific workflows autonomously — with human oversight as needed.
Autonomous Experimentation
Developing closed-loop platforms where AI reasons over experimental data and proposes next steps — accelerating the scientific discovery cycle.
Materials & Scientific AI
Applying machine learning to inverse design, property prediction, and synthesis planning for advanced materials — including MXenes, perovskites, and nanomaterials.
Human–Machine Interfaces
Researching how to design interfaces that give humans interpretable, actionable visibility into AI behavior — especially in physical robotics contexts.
How we do research
Experiment-first mindset
We prioritize building and testing physical systems early — because real-world behavior always surprises what simulation or theory predicts.
Every research thread at Cogninoid Labs starts with a question and ends with something you can touch, run, or measure.
Recent notes
On closed-loop intelligence
Notes on why static training datasets are insufficient for physical AI — and why the feedback loop between sensing, reasoning, and actuation is the core problem.
Designing robotic hardware for AI control
Reflections on building robot arms and grippers that are explicitly designed for AI-driven control — not just traditional position-control teleoperation.
Agentic systems for scientific workflows
Early experiments with LLM-based agents that can plan and execute multi-step lab-style workflows — with tool use and error recovery.