Robotic dexterity is an error recovery problem

A single-action success rate of 99 percent sounds deployable until you chain 100 physical steps together into an actual workflow. At that rate, the probability of finishing the sequence without intervention drops to 36.6 percent. Reaching autonomous operation requires pushing toward four nines of reliability, a mathematical reality often ignored when evaluation centers on isolated demo tasks.

Writing for The Robot Report, Nicolas Sauvage argues that useful dexterity is a closed-loop system problem rather than a race for more fingers and degrees of freedom. Customers do not buy humanoid hands; they buy completed tasks. In practical environments, dexterity comes down to sensing contact, catching slips early, and recovering before a slight miscalculation ruins the entire sequence.

This is why minimum sufficient dexterity routinely outperforms anthropomorphic hardware on the floor. Purpose-built end-effectors paired with fast tactile feedback loops achieve acceptable reliability far faster than twenty-joint hands trying to mirror human anatomy. Until physical AI models can reliably detect and correct their own physical errors, adding mechanical complexity to the gripper just multiplies the failure modes.