Using Geometric Features to Represent Near-Contact Behavior in Robotic Grasping

Dessalene, Eadom; Ong, Yi Herng; Morrow, John; Balasubramanian, R.; Grimm, Cindy

doi:10.1109/icra.2019.8793779

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Newbury et al [49] used two Convolutional Neural Networks (CNNs) to estimate both the placement rotations and stabilities and obtain the humanpreferred object placements and orientations. CNNs are also well used to estimate the grasp configurations [50] and predict the grasp qualities [51] as well. Feng et al [52] used a Support Vector Machine (SVM) and a Long Short-Term Memory (LSTM) model to analyze the features of tactile sensors to detect slip and unstable grasps.…”

Section: B Placement Estimationmentioning

confidence: 99%

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Wan

Koyama

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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Section: B Placement Estimationmentioning

confidence: 99%

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Wan

Koyama

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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“…In light of this, we propose a supervised learning method to learn grasping movements according to the shape of the object. Previous studies have shown that the size of the object along the grasped dimension is closely related to grasping postures (Dessalene et al, 2019;Starke et al, 2020). Therefore, this was used as a shape descriptor to establish the relationship with grasping movements.…”

Section: Learning Postural Synergiesmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: B Placement Estimationmentioning

confidence: 99%

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Hu¹,

Wan²,

Koyama³

et al. 2021

Preprint

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This paper presented an regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF's geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method's ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method's robustness and efficacy.

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Using Geometric Features to Represent Near-Contact Behavior in Robotic Grasping

Cited by 5 publications

References 14 publications

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

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

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

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