ArticlePLoS computational biology2020
Predicting precision grip grasp locations on three-dimensional objects.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Relational dynamics inform predictive motor planning and perception.Journal of neurophysiology · 2025Article
- Phase-Dependent Visual and Sensorimotor Integration of Features for Grasp Computations before and after Effector Specification.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Human manipulation strategy when changing object deformability and task properties.Scientific reports · 2024Article
- Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience.Nature human behaviour · 2024Article
- High-level aftereffects reveal the role of statistical features in visual shape encoding.Current biology : CB · 2024Article
- Distinct Neural Components of Visually Guided Grasping during Planning and Execution.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- EEG Decoding Reveals Neural Predictions for Naturalistic Material Behaviors.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- HANDdata - first-person dataset including proximity and kinematics measurements from reach-to-grasp actions.Scientific data · 2023Article
- Having several options does not increase the time it takes to make a movement to an adequate end point.Experimental brain research · 2022Article
- An image-computable model of human visual shape similarity.PLoS computational biology · 2021Article
- Stereopsis contributes to the predictive control of grip forces during prehension.Experimental brain research · 2021Article
- An elasticity-curvature illusion decouples cutaneous and proprioceptive cues in active exploration of soft objects.PLoS computational biology · 2021Article
- Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We rarely experience difficulty picking up objects, yet of all potential contact points on the surface, only a small proportion yield effective grasps. Here, we present extensive behavioral data alongside a normative 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. We employed these data to construct a model which combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, the model predicts individual grasps almost as well as different individuals predict one another's, but fitting weights reveals the relative importance of the different constraints. The model also accurately predicts human grasps on novel 3D-printed objects with more naturalistic geometries and is robust to perturbations in its key parameters. Together, the findings provide a unified account of how we successfully grasp objects of different 3D shape, orientation, mass, and mass distribution.
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