Dynamic object manipulation using a virtual frame by a triple soft-fingered robotic hand

Tahara, Kenji; Arimoto, Suguru; Yoshida, Morio

doi:10.1109/robot.2010.5509372

Cited by 78 publications

(78 citation statements)

References 10 publications

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“…To this end, we follow the idea presented in our previous work [41] that learns a probabilistic representation using the concept of Virtual Frame(VF) [57]. A VF defined in the hand frame can be expressed as:…”

Section: Probabilistic Model For Hand Inverse Kinematicsmentioning

confidence: 99%

Dexterous grasping under shape uncertainty

Hang

Kragić

et al. 2016

Robotics and Autonomous Systems

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An important challenge in robotics is to achieve robust performance in object grasping and manipulation, dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in object shape is parameterized and incorporated as a constraint into grasp planning. The proposed approach is used to plan feasible hand configurations for realizing planned contacts using different robotic hands. A compliant finger closing scheme is devised by exploiting both the object shape uncertainty and tactile sensing at fingertips. Experimental evaluation demonstrates that our method improves the performance of dexterous grasping under shape uncertainty.

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Section: Probabilistic Model For Hand Inverse Kinematicsmentioning

confidence: 99%

Dexterous grasping under shape uncertainty

Hang

Kragić

et al. 2016

Robotics and Autonomous Systems

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“…The sensed contact locations can be used to define a 'virtual object frame' (the centroid of contacts), to act as a proxy for the object's location, helping in executing in-grasp manipulations [13][14][15][16]. A similar method is used in [17] for local control, augmented by a finger gaiting strategy for larger movements.…”

Section: B In-hand Manipulation Using Sensor Feedbackmentioning

confidence: 99%

“…Tactile and haptic feedback are especially useful for in-hand manipulation. Apart from providing information on the object's pose and contact normals [11][12][13][14][15][16][17], tactile sensing can provide greater robustness to variations in object properties [18][19][20][21], perturbations [22,23], and sensing errors [10,18]. Tactile sensing can also aid by detecting grasp instabilities and slippage that can occur while manipulating an object [13,20,24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Tactile and haptic sensors have been shown to improve grasping performance [10, 18-21, 24, 25], and are promising for in-hand manipulation. Strategies for feedback-based inhand manipulation [13][14][15][16][17] have been applied to unknown objects, but commonly only work for fully actuated hands with known dynamics and kinematics models. Exact models, however, are often not available for compliant robots.…”

Section: Introductionmentioning

confidence: 99%

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Learning robot in-hand manipulation with tactile features

Hoof

Hermans

Neumann

et al. 2015

2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

122

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Abstract-Dexterous manipulation enables repositioning of objects and tools within a robot's hand. When applying dexterous manipulation to unknown objects, exact object models are not available. Instead of relying on models, compliance and tactile feedback can be exploited to adapt to unknown objects. However, compliant hands and tactile sensors add complexity and are themselves difficult to model. Hence, we propose acquiring in-hand manipulation skills through reinforcement learning, which does not require analytic dynamics or kinematics models. In this paper, we show that this approach successfully acquires a tactile manipulation skill using a passively compliant hand. Additionally, we show that the learned tactile skill generalizes to novel objects.

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“…If developing such robot hand systems, we should take the affinity to human into consideration. Therefore, grasping with deformable fingertips or contact areas have been researched [2]. They developed controller for grasping and manipulating the object with deformable fingertips.…”

Section: Introductionmentioning

confidence: 99%

Softness effects on manipulability and grasp stability

Watanabe¹

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a novel analysis for the effects of softness at the fingertip on the manipulability and stability of grasping. The stability for grasping can be regarded that how much magnitude of external wrench we can balance. We formulate manipulability and the set of generable object wrenches for grasping system, taking deformation of the fingertips into consideration, and show that the increase of the softness decreases the manipulability while it increases generable object wrench. The validity of our analysis is shown by numerical examples.

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Dynamic object manipulation using a virtual frame by a triple soft-fingered robotic hand

Cited by 78 publications

References 10 publications

Dexterous grasping under shape uncertainty

Dexterous grasping under shape uncertainty

Learning robot in-hand manipulation with tactile features

Softness effects on manipulability and grasp stability

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