This paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size.

We present a human-subjects study approach that supports the analysis of the manipulation performance of robotic hands that have the same morphology but different actuation and compliance. Specifically, we use this approach to analyze three different types of hands (one underactuated, one fully actuated, one fully actuated with compliant distal joints) as they are used to perform two manipulation tasks. The first task uses a power grasp (spraying with a spray bottle), the second a precision grasp (tracing a line on a bowl with a pen). We show that compliance in the distal joints significantly improves performance and task completion. We also show that humans choose significantly different poses for the same task when using a fully-actuated versus underactuated hand, which also results in superior task performance. Our results suggest that humans use a combination of under-actuated and fully-actuated techniques, which when used on robotic systems would also improve their performance on manipulation tasks.