That sounds ambitious, but don’t let the hype drown the detail. Start by segmenting the system into discrete ratio blocks, each with its own microcontroller and torque sensor. Calibrate each block individually under controlled loads, then validate the transitions between them with a high‑resolution encoder. Remember, the real precision comes from isolating the variables, not from a clever algorithm alone. If you can keep each module independent and test them in isolation, the whole system will behave predictably even under changing loads.

A single adaptive clutch sounds sleek, but think of it as a high‑speed autopilot that never double‑checks. Learning on the fly means you’ll be balancing raw speed against safety margins—microseconds of lag in a torque sensor can translate to a clutch slip or, worse, a runaway. Start with a simple PID that adjusts clutch pressure based on a high‑resolution torque transducer, then add a neural tweak only after you’ve proven the hardware can react consistently. The weight savings will come from eliminating the extra boxes, but you’ll trade that for a much tighter tolerance on every component. If you can keep the sensor latency below the clutch’s mechanical response time, it’s doable—just don’t let the novelty make you skip the rigorous calibration.