Dataset Information

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands.

ABSTRACT: The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.

SUBMITTER: Liu B

PROVIDER: S-EPMC8480411 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:As numerous movement options are available in reaching and grasping, of particular interest are what factors influence an individual's choice of action. In the current study a preferential reaching task was used to assess the propensity for right handers to select their preferred hand and grasp a coffee mug by the handle in both independent and joint action object manipulation contexts. Mug location (right-space, midline, and left-space) and handle orientation (toward, away, to left, and to right of the participant) varied in four tasks that differed as a function of intention: (1) pick-up (unimanual, independent); (2) pick-up and pour (bimanual, independent); (3) pick-up and pass (unimanual, joint action); and (4) pick-up, pour and pass (bimanual, joint action). In line with previous reports, a right-hand preference for unimanual tasks was observed. Furthermore, extending existing literature to a preferential reaching task, role differentiation between the hands in bimanual tasks (i.e., preferred hand mobilizing, non-preferred hand stabilizing) was displayed. Finally, right-hand selection was greatest in right space, albeit lower in bimanual tasks compared to what is typically reported in unimanual tasks. Findings are attributed to the desire to maximize biomechanical efficiency in reaching. Grasp postures were also observed to reflect consideration of efficiency. More specifically, within independent object manipulation (pick-up; pick-up and pour) participants only grasped the mug by the handle when it afforded a comfortable posture. Furthermore, in joint action (pick-up and pass; pick-up, pour and pass), the confederate was only offered the handle if the intended action of the confederate was similar or required less effort than that of the participant. Together, findings from the current study add to our knowledge of hand and grasp selection in unimanual and bimanual object manipulation, within the context of both independent and joint action tasks.

Dataset Information

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands.

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