Industrial robotic dexterity will not advance through better algorithms, sensors, or hardware alone. It requires a measurable way to evaluate how robotic systems perform in real-world conditions, where perception, motion, contact, AI models, and control must work together reliably.

The key question is not whether a robot can complete a task once in a controlled demonstration. It’s whether the industry is making measurable progress on industrial manipulation challenges that have resisted automation for decades. Answering that question motivated the creation of the Industrial Dexterity Benchmark program at ADI.

Why Benchmarks Are Critical

As robotics shifts from explicitly programmed systems to systems that learn and generalize from data, objective evaluation becomes increasingly important. Without common benchmarks, it is difficult to determine whether a new approach represents genuine reproducible progress or simply performs well in a limited scenario. A shared yardstick enables meaningful comparisons and helps guide progress and innovation.