Abstract
We rarely experience difficulty picking up objects, yet of all potential grasp points on an object’s surface, only a small proportion yield stable, comfortable grasps. Here, we present extensive behavioral data alongside a computational model that correctly predicts human precision grasping of unfamiliar 3D objects. We tracked participants’ forefinger and thumb as they picked up objects of 10 wood and brass cubes configured to tease apart effects of shape, weight, orientation, and mass distribution. Grasps were highly systematic and consistent across repetitions and participants. The model combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, we find that the model predicts human grasps with striking fidelity: indeed, it predicts individual grasps almost as well as different individuals predict one another’s. Adding fittable weights to the model reveals the relative importance of the different constraints: the combination of force closure, hand posture, and grasp size explains most of human grasping behavior, while our participants cared surprisingly little about minimizing torque and optimizing object visibility. Together, these findings provide a unified account of how we derive effective grasps from objects’ 3D shape and material properties to interact with them successfully.
Significance Statement Working out how we pick up and interact with objects effectively is one of the most important challenges in behavioral science. Of all the potential contact points on an object’s surface, only a small proportion yield effective grasps. Despite this, we rarely experience any difficulty choosing where and how to pick objects up. Here, we present a computational model that unifies the varied and fragmented literature on human grasp selection. We find that the model correctly predicts human grasps across a wide variety of conditions, taking into account the object’s 3D shape, material properties and orientation.
Footnotes
Author Contributions: GM, LKK, VCP and RWF conceived and designed the study. LKK collected the data. LKK and GM analyzed the data. GM developed the computational model of grasp selection. All authors wrote the manuscript.