Human-Inspired Robotic Grasp Control With Tactile Sensing

Romano, Joseph M.; Hsiao, Kaijen; Niemeyer, G.; Chitta, Sachin; Kuchenbecker, Katherine J.

doi:10.1109/tro.2011.2162271

Cited by 354 publications

(213 citation statements)

References 20 publications

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“…While these studies focus more on the grasp control after the fingers are closed, rather few works have been reported on devising control algorithms for the finger approaching and closing stage. Previous works usually assume that a position controller is first used for approaching and then it is switched to a force controller once contact or collision is detected [26,42]. However, the switching may easily cause the overshooting of the contact force due to the limitation in sensing and low control rate.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Dexterous grasping under shape uncertainty

Hang

Kragić

et al. 2016

Robotics and Autonomous Systems

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“…Multiple researchers, e.g. [13,20,24,25], focused on detecting slippage and instabilities. Furthermore, [13] subsequently used tactile sensing to find stable grasps similar to the current grasp, and adapt the current grasp.…”

Section: B In-hand Manipulation Using Sensor Feedbackmentioning

confidence: 99%

“…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]. Tactile and haptic sensors have been shown to improve grasping performance [10, 18-21, 24, 25], and are promising for in-hand manipulation.…”

Section: Introductionmentioning

confidence: 99%

Learning robot in-hand manipulation with tactile features

Hoof

Hermans

Neumann

et al. 2015

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

127

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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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“…(1) Compliant Landing: fingers are placed in a pre-defined grasp pre-shape and the hand is maneuvered downward until all fingers fully rest on the support surface, (2) Compliant Grasping: force feedback is used to maintain a desired contact force at the fingertips while the fingers joints are synchronously closed to capture the object, (3) Lift and Transportation: the object is lifted away from the surface and carried to the destination, (4) Compliant Release: the object is gently deposited on the support surface using a method similar to step 2. A similar sequence of grasping controls has been suggested in [16] inspired by neuroscience studies, but it lacks the finger landing step and the compliant primitives proposed in this paper. …”

Section: Force Compliant Grasping Primitivesmentioning

confidence: 99%

Robust Object Grasping using Force Compliant Motion Primitives

Kazemi¹,

Valois²,

Bagnell³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-We address the problem of grasping everyday objects that are small relative to an anthropomorphic hand, such as pens, screwdrivers, cellphones, and hammers from their natural poses on a support surface, e.g., a table top. In such conditions, state of the art grasp generation techniques fail to provide robust, achievable solutions due to either ignoring or trying to avoid contact with the support surface. In contrast, we show that contact with support surfaces is critical for grasping small objects. This also conforms with our anecdotal observations of human grasping behaviors. We develop a simple closed-loop hybrid controller that mimics this interactive, contact-rich strategy by a position-force, pre-grasp and landing strategy for finger placement. The approach uses a compliant control of the hand during the grasp and release of objects in order to preserve safety. We conducted extensive grasping experiments on a variety of small objects with similar shape and size. The results demonstrate that our approach is robust to localization uncertainties and applies to many everyday objects.

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Human-Inspired Robotic Grasp Control With Tactile Sensing

Cited by 354 publications

References 20 publications

Dexterous grasping under shape uncertainty

Dexterous grasping under shape uncertainty

Learning robot in-hand manipulation with tactile features

Robust Object Grasping using Force Compliant Motion Primitives

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