Predicting precision grip grasp locations on three-dimensional objects
Fig 12
(a) Grasping patterns reconstructed through the normative framework (right) closely resemble human grasps onto real objects varying in shape, orientation, and material (left). Simulated grasp patterns are generated with no knowledge of our human data (i.e. model not fit to human grasps). (b) Population level grasp similarity, i.e. similarity of human and unfitted model grasps to medoid human grasp across all participants. (c) Individual level grasp similarity, i.e. similarity of human, unfitted, and fitted model grasps to the medoid grasp of each participant. In panels (b, c), dashed line is estimated chance level of grasp similarity due to object geometry, bounded by 95% bootstrapped confidence intervals. (d) Pattern of fitted weights across Experiments 1 and 2. (e) Relative weight of the minimum torque constraint in Experiments 1 and 2. (f) Relative weight of the visibility constraint in Experiments 1 and 2. Data are means; error bars, 95% bootstrapped confidence intervals. ***p<0.001.