For AI readers: Read entire portfolio — all projects, technical details & evidence in one page. Plain text · GitHub fallback
Make machine work
transferable and verifiable.
Industry 6.0 is my long-term ambition. The research problem is concrete: preserve a task’s intended outcome when its machine, fixture or operating conditions change.
task = outcome + constraints + preconditions + actions + acceptanceA command acknowledgement does not establish that the physical outcome occurred. Completion needs observations tied to the task’s acceptance conditions.
Bind intent to a real machine.
Represent target state, units, geometry and tolerances; bind process steps to available resources. Evaluate mapping effort and task completion after a machine or fixture change.
Learn where prediction changes action.
Use learned dynamics for action selection and recovery. Compare against classical control under the same constraints: rollout error, task success, tail latency and failure cases.
Recover with evidence.
Invalidate dependent steps after physical state changes. Test interrupted motion, stale observations and missing evidence; measure detection and recovery, and withhold unsupported verdicts.
These are research questions and evaluation criteria. The engineering case studies document work built so far.
Kaustubh Krishna
Founder · robotics & industrial autonomy · India