Power grasp planning for anthropomorphic robot hands

Roa, Máximo A.; Argus, Max; Leidner, Daniel; Borst, Christoph; Hirzinger, Gerd

doi:10.1109/icra.2012.6225068

Cited by 46 publications

(31 citation statements)

References 23 publications

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“…Both precision and power grasps can be force closure grasps. Analytical approaches in [13,14] plan power grasps and precision grasps in [15,16,22]. Vahrenkamp et al [23] propose a grasp planner to plan both power and precision grasps that generates grasp hypotheses based on the global and local geometric surface features from the object mesh.…”

Section: Related Workmentioning

confidence: 99%

Modeling Grasp Type Improves Learning-Based Grasp Planning

Hermans

2019

IEEE Robot. Autom. Lett.

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Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multi-fingered power grasp offering stability, while holding a pen to write requires a multi-fingered precision grasp to impart dexterity on the object. In this paper, we propose a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real-time. We take a learning approach in order to plan grasps of different types for previously unseen objects when only partial visual information is available. Our work demonstrates the first supervised learning approach to grasp planning that can explicitly plan both power and precision grasps for a given object. Additionally, we compare our learned grasp model with a model that does not encode type and show that modeling grasp type improves the success rate of generated grasps. Furthermore we show the benefit of learning a prior over grasp configurations to improve grasp inference with a learned classifier.

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Section: Related Workmentioning

confidence: 99%

Modeling Grasp Type Improves Learning-Based Grasp Planning

Hermans

2019

IEEE Robot. Autom. Lett.

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“…In [11], after heuristically generating a set of feasible grasp candidates on the basis of the characteristics of the object, grasp quality measures have been used to choose the best grasp. In [12], the best location of the robotic hand in the space of feasible grasp configurations is researched. The main limitations of this approach are the high dimension of the solution space in the grasp optimization and the need for computing the hand inverse kinematics in order to guarantee that the object contact points are physically reachable by the robotic hand.…”

Section: Introductionmentioning

confidence: 99%

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Cordella

Zollo

Salerno

et al. 2014

International Journal of Advanced Robotic Systems

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Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.

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“…The field of object placement planning in the context of pick-and-place applications [2] was not as such much focused on as was the grasp planning [3]- [5] during the past years. First, we mention approaches dealing with subproblems and related approaches.…”

Section: Related Workmentioning

confidence: 99%

A geometrical placement planner for unknown sensor-modelled objects and placement areas

Baumgartl

Kaminsky

Henrich

2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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A Personal Robot should be able to handle possible unknown objects in unknown environments. For a manipulation task the question what to do with an object once it had been grasped is one of the most essential ones beside the grasping task itself.We propose a placement planner for sensor-modelled objects in complex environments. The planner computes a stable position and orientation for the object in the environment. The algorithm uses only geometric information, most notably no force or torque sensor is required. In particular, we introduce a novel approach regarding the configuration computation.By means of experiments with various household objects the robustness and performance are validated. Further on, we compare our approach to a configuration computation using a physics simulation framework.

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Power grasp planning for anthropomorphic robot hands

Cited by 46 publications

References 23 publications

Modeling Grasp Type Improves Learning-Based Grasp Planning

Modeling Grasp Type Improves Learning-Based Grasp Planning

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

A geometrical placement planner for unknown sensor-modelled objects and placement areas

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