In this paper we define two feature representations for grasping. These representations capture hand-object geometric relationships at the near-contact stage - before the fingers close around the object. Their benefits are: 1) They are stable under noise in both joint and pose variation. 2) They are largely hand and object agnostic, enabling direct comparison across different hand morphologies. 3) Their format makes them suitable for direct application of machine learning techniques developed for images. We validate the representations by: 1) Demonstrating that they can accurately predict the distribution of ε-metric values generated by kinematic noise. I.e., they capture much of the information inherent in contact points and force vectors without the corresponding instabilities. 2) Training a binary grasp success classifier on a real-world data set consisting of 588 grasps.

Grasping a simple object from the side is easy - unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans - when faced with this challenge - adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either “puppetteering” a robotic manipulator that is identical (geometrically and kinematically) to a popular underactuated robotic manipulator (the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware.

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.