Grasping new material densities
Grasping new material densities
When picking up objects, we prefer stable grips with minimal torque by seeking grasp points that straddle the object’s center of mass (CoM). For homogeneous objects, the CoM is at the geometric center (GC), computable from shape cues. However, everyday objects often include components of different materials and densities. In this case, the CoM depends on the object’s geometry and the components’ densities.
We asked how participants estimate the CoM of novel, two-part objects. Across 4 experiments, participants used a precision grip to lift cylindrical objects comprised of steel and PVC in varying proportions (steel 3 times denser than PVC). In all experiments, initial grasps were close to objects’ GCs; neither every-day experience (metals are denser than PVC) nor pre-exposure to the stimulus materials in isolation moved first grasps away from the GC. Within a few trials, however, grasps shifted towards the CoM, reducing but not eliminating torque. Learning transferred across the stimulus set, i.e., observers learnt the materials’ densities (or their ratio) rather than learning each object’s CoM. In addition, there was a stable ‘under-reaching’ bias towards the grasping hand.
An ‘inverted density’ stimulus set (PVC 3 x denser than steel) induced similarly fast learning, confirming that prior knowledge of materials has little effect on grasp point selection. When stimulus sets were covertly switched during an experiment, the unexpected force feedback caused even faster grasp adaptation.
Torque minimisation is a strong driver of grasp point adaptation, but there is a surprising lack of transfer following pre-exposure to relevant materials.
Grasping behaviour, visiuomotor learning, composite objects, novel materials, density estimation
10.1163-22134808-bja10146
Adams, Wendy J.
25685aaa-fc54-4d25-8d65-f35f4c5ab688
Mehraeen, Sina
c69386cb-db42-486e-8131-45fb2cfecab4
Ernst, Marc O.
18c46bf7-2f74-4850-9434-8ef6cb255d51
Adams, Wendy J.
25685aaa-fc54-4d25-8d65-f35f4c5ab688
Mehraeen, Sina
c69386cb-db42-486e-8131-45fb2cfecab4
Ernst, Marc O.
18c46bf7-2f74-4850-9434-8ef6cb255d51
Adams, Wendy J., Mehraeen, Sina and Ernst, Marc O.
(2025)
Grasping new material densities.
Multisensory Research.
(doi:10.1163-22134808-bja10146).
Abstract
When picking up objects, we prefer stable grips with minimal torque by seeking grasp points that straddle the object’s center of mass (CoM). For homogeneous objects, the CoM is at the geometric center (GC), computable from shape cues. However, everyday objects often include components of different materials and densities. In this case, the CoM depends on the object’s geometry and the components’ densities.
We asked how participants estimate the CoM of novel, two-part objects. Across 4 experiments, participants used a precision grip to lift cylindrical objects comprised of steel and PVC in varying proportions (steel 3 times denser than PVC). In all experiments, initial grasps were close to objects’ GCs; neither every-day experience (metals are denser than PVC) nor pre-exposure to the stimulus materials in isolation moved first grasps away from the GC. Within a few trials, however, grasps shifted towards the CoM, reducing but not eliminating torque. Learning transferred across the stimulus set, i.e., observers learnt the materials’ densities (or their ratio) rather than learning each object’s CoM. In addition, there was a stable ‘under-reaching’ bias towards the grasping hand.
An ‘inverted density’ stimulus set (PVC 3 x denser than steel) induced similarly fast learning, confirming that prior knowledge of materials has little effect on grasp point selection. When stimulus sets were covertly switched during an experiment, the unexpected force feedback caused even faster grasp adaptation.
Torque minimisation is a strong driver of grasp point adaptation, but there is a surprising lack of transfer following pre-exposure to relevant materials.
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Accepted/In Press date: 1 April 2025
e-pub ahead of print date: 23 April 2025
Keywords:
Grasping behaviour, visiuomotor learning, composite objects, novel materials, density estimation
Identifiers
Local EPrints ID: 501078
URI: http://eprints.soton.ac.uk/id/eprint/501078
ISSN: 2213-4808
PURE UUID: f21f0f19-4d1e-49d7-8d55-09392b245e66
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Date deposited: 22 May 2025 16:45
Last modified: 22 Aug 2025 01:52
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Author:
Sina Mehraeen
Author:
Marc O. Ernst
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