On grasp choice, grasp models, and the design of hands for manufacturing tasks

Cutkosky, Mark R.

doi:10.1109/70.34763

Cited by 1,289 publications

(714 citation statements)

References 19 publications

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“…We first focus on a reduced version of the problem, namely we consider a subset of three grasps: pinch grip, small power grasp, and large power grasp (second, fourth and fifth panels from the left of Figure 2). These grasps are very different from one another from a kinematic point of view; this is reflected in their distance in standard grasp taxonomies (examples can be found in [14,22]). Consider first Figure 6, depicting the grasps in 3 dimensions (first, second and third synergy) in the kinematic (left, plotting data from X * glove ) and sEMG (right, plotting data from X * EM G ) spaces.…”

Section: Common Synergy Featuresmentioning

confidence: 99%

Evidence of muscle synergies during human grasping

Castellini

Smagt

2013

Biol Cybern

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Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas so far the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e., that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.

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Section: Common Synergy Featuresmentioning

confidence: 99%

Evidence of muscle synergies during human grasping

Castellini

Smagt

2013

Biol Cybern

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“…Power grasps and precision grasps are distinguished [20]. A precision grasp is a grasp where only the finger tips are in contact with the object.…”

Section: Grasp Planningmentioning

confidence: 99%

Knowledge Representations for Planning Manipulation Tasks

Zacharias

2012

Cognitive Systems Monographs

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A complex manipulation task for a robot has to be planned on different levels of abstraction before it can be executed on a real system. At a high-level of abstraction, a task planner generates a sequence of subtasks from a given abstract command, its pre-and postconditions and a current world model. To restrict the already huge search space of the task planner, aspects like geometric information are not contained in the world model at this level. The task planner then assigns the subtasks to planners on a lower level of abstraction like path planners or grasp planners. These planners rely on geometric information to compute their solutions. However, without considering geometric information the task planner cannot determine good parameters for these low-level planners. Therefore, the derived plans may not be valid for a given situation and may require backtracking. Knowledge representations can bridge the gap between these planning levels and thereby enable scene reasoning. The kinematic capabilities of a robot are one aspect the task planner has to be aware of. Therefore this thesis introduces a novel general representation of the kinematic capabilities of a robot arm. The versatile workspace is defined to describe in which orientations the end effector attached to a robot arm can reach a position. The capability map is a calculable representation of the versatile workspace that allows to determine how well regions of the workspace are reachable. It accurately represents the versatile workspace of arbitrary arm kinematics and enables autonomous taskspecific reasoning. Furthermore, its visualization scheme is intuitively comprehensible for the human. The versatile applicability of the capability map is shown by examples from several distinct application domains. In human-robot interaction, the capability map is used to objectively evaluate a bi-manual interface for tele-operation. The results can also supplement the design process for humanoid robot design. In low-level geometric planning, the capability map is used to reduce the search space and to parameterize path planners and grasp planners. Thus, in a manipulation task for a humanoid robot more human-like motion is planned while simultaneously the computation time is reduced. In high-level task reasoning, the capability map is used to evaluate how well a robot is suited for a task. In an example a humanoid robot has to perform a task involving 3D trajectories. Regions are extracted from the capability map that permit the task execution and the suitability of the robot is inferred.

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“…This is reflected in grasp taxonomies, classifying grasps according to the final hand posture attained after the grasp process is completed (Cutkosky, 1989;Feix et al, 2009). Even the early work on postural synergies, which has had a profound impact on robotics, initially only considered synergies of static grasp postures (Santello et al, 1998).…”

Section: Force Closurementioning

confidence: 99%

Exploitation of Environmental Constraints in Human and Robotic Grasping

Deimel¹,

Eppner²,

Álvarez-Ruiz³

et al. 2016

Springer Tracts in Advanced Robotics

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We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints.

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On grasp choice, grasp models, and the design of hands for manufacturing tasks

Cited by 1,289 publications

References 19 publications

Evidence of muscle synergies during human grasping

Evidence of muscle synergies during human grasping

Knowledge Representations for Planning Manipulation Tasks

Exploitation of Environmental Constraints in Human and Robotic Grasping

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