Grasp quality evaluation in underactuated robotic hands

Pozzi, María; Sundaram, Ashok M.; Malvezzi, Monica; Prattichizzo, Domenico; Roa, Máximo A.

doi:10.1109/iros.2016.7759307

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…In the case of multifingered underactuated hands, there can be several contact points in one finger that are controlled through a single actuator, or even all the finger contacts can be individually controlled by one actuator [15]. To consider this combination in the computation of GW S, an actuation matrix M ∈ R na×nc is defined for each grasp, where n a is the number of actuators.…”

Section: Consideration Of the Hand Actuationmentioning

confidence: 99%

Planning realistic interactions for bimanual grasping and manipulation

Sundaram

Porges

Roa

2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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This work presents a dual arm grasp planning architecture that includes two relevant aspects often neglected: differences in hand actuation, and realistic forces applicable by the end effectors. The introduction of an actuation matrix allows considering differences in contact forces that can be generated between, for instance, a fully actuated and an underactuated hand. The consideration of realistic forces allows the computation of real magnitudes of forces and torques that can be resisted by the grasped object. The manipulability workspace can also be computed based on the capability maps, thus providing all the possible motions that can be imparted on the grasped object while respecting the dual hand grasp constraints. The joint consideration of these factors allow the selection of a good grasp for a desired bimanual manipulation.

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Section: Consideration Of the Hand Actuationmentioning

confidence: 99%

Planning realistic interactions for bimanual grasping and manipulation

Sundaram

Porges

Roa

2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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“…Grasping metrics belong to the approaches based on physics models for grasping, which assume that all perceptual information (kinematics and dynamics) is fully observable, and usually does not consider uncertainty due to computational complexity issues. Although limited in application scope due to the reality gap of model assumptions, the grasping metrics allow to (i) evaluate the mechanical design of robotic hands in simulation [18] and (ii) predict results of actual grasping experiments [17]. Thus, metrics of the physical models are a useful tool for designing mechanics and control without prototyping.…”

Section: Introductionmentioning

confidence: 99%

“…The Potential Grasp Robustness (PGR) metric considers underactuated hands [18]. This metric assumes that each contact point is in one of the three states as follows: (i) Fully attached (positive constraint and Couloumb constraint), (ii) only positive constraint and (iii) deatach.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and heuristics for underactuated hands: grasp pose selection based on the ppotential grasp robustness metric

Almeida¹,

Moreno²

2021

SN Appl. Sci.

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The Potential Grasp Robustness (PGR) metric considers different states of the contact points, relaxing the requirement of all points being far from the friction cone boundary. The addition of new states for each contact point increases the computational complexity, which is combinatorial on the number of states and takes a long time for grasping configurations with large number of hand-object contacts. In this work we analyse the computational complexity of two recently proposed heuristics, which consider that: (i) the minimum number of contact points needed could be in two states and (ii) an analysis of grasp contact data provides the most common combinations of contact points that lead to an accurate estimation of PGR. For selecting grasp configurations, the PGR computation approach is not robust because assumes that measured forces at the contact points do not have uncertainty. In addition to the heuristics, we propose a new uncertainty based metric, the coefficient of variation of PGR. The grasp selection experiments show that the coefficient of variation provides similar results to the pose variation metric. The grasp selection that uses the uncertainty based computation of PGR find more stable contact points than the maximization of the conventional PGR. Article highlights Development of new heuristics for computation of grasp metrics of underactuated hands. Definition of uncertainty-based metrics for grasp se- lection Reduction of reality gap for physics-based grasping metrics of underactuated hands

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“…modelbased [2] versus model-free [3], and adopt widely differing approaches, e.g. analytic [4], [5], data-driven [6], [7], or based on human demonstration [8]. Other work explores mechanically adaptive hands that simplify the grasping process thanks to their inherent mechanical adaptability [9], [10], or combines different sensing modalities for performing grasping and in-hand manipulation [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Protocol for Grasp Planning Algorithms

Bekiroglu

Marturi

Roa

et al. 2020

IEEE Robot. Autom. Lett.

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Numerous grasp planning algorithms have been proposed since the 1980s. The grasping literature has expanded rapidly in recent years, building on greatly improved vision systems and computing power. Methods have been proposed to plan stable grasps on: known objects (exact 3D model is available), familiar objects (e.g. exploiting a-priori known grasps for different objects of the same category), or novel object shapes observed during task execution. Few of these methods have ever been compared in a systematic way, and objective performance evaluation of such complex systems remains problematic. Difficulties and confounding factors include: different assumptions and amounts of a-priori knowledge in different algorithms; different robots, hands, vision systems and setups in different labs; different choices or application needs for grasped objects. Also, grasp planning can use different grasp quality metrics (including empirical or theoretical stability measures), or other criteria, e.g. computational speed, or combination of grasps with reachability considerations. While acknowledging and discussing the outstanding difficulties surrounding this complex topic, we propose a methodology for reproducible experiments to compare the performance of a variety of grasp planning algorithms. Our protocol attempts to improve the objectivity with which different grasp planners are compared by minimising the influence of key components in the grasping pipeline, e.g. vision and pose estimation. The protocol is demonstrated by evaluating two different grasp planners: a state-of-the-art model-free planner, and a popular open-source model-based planner. We show results from real-robot experiments with a 7-DoF arm and 2-finger hand, and simulation-based evaluations.

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Grasp quality evaluation in underactuated robotic hands

Cited by 12 publications

References 24 publications

Planning realistic interactions for bimanual grasping and manipulation

Planning realistic interactions for bimanual grasping and manipulation

Uncertainty and heuristics for underactuated hands: grasp pose selection based on the ppotential grasp robustness metric

Benchmarking Protocol for Grasp Planning Algorithms

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