Adaptive grasping by multi fingered hand with tactile sensor based on robust force and position control

Takahashi, Tomoya; Tsuboi, Toshimitsu; Kishida, Tomoki; Kawanami, Yasunori; Shimizu, Shohei; Iribe, Masatsugu; Fukushima, Toru; Fujita, Masahiro

doi:10.1109/robot.2008.4543219

Cited by 57 publications

(36 citation statements)

References 13 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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“…Smooth compliance in response to object perturbations when grasping necessitates a tight coordination between all fingers, else the grasped object might fall from the hand. Moreover, this coordination is typically ensured by a good knowledge of the hand kinematics and of the object shape [5], [6], [7], [8]. To tackle this issue, rather than handcraft the coordination patterns across all fingers for each novel object, we adopt a learning approach based on human demonstration.…”

Section: Fig 1 A) Grasp Adaptationmentioning

confidence: 99%

“…Furthermore, recent work has shown the necessity of having access to a rich set of sensory information in order to perform manipulation tasks of increasing complexity [21], [22], [5], [23]. Through the use of such advanced sensing devices, touch-based exploratory methods have developed that discover and learn object properties and manipulation strategies [19], [24], [25].…”

Section: A Dexterous Manipulationmentioning

confidence: 99%

“…The continued development of sensor technologies with increasing sophistication [21], [22], [5], [23] promoted the use of adaptive control schemes such as force and impedance control [16], [7], and later hybrid methods [5]. These approaches are hierarchically combined with high-level and predefined behavioral laws that triggers the controllers when appropriate.…”

Section: B Reactive Grasping and Contact Maintenancementioning

confidence: 99%

“…Security reflexes are employed to recover a loss of contact [32], and fuzzy logic rules specify a change in contact normal based on perceived forces [33]. Contact recovery behaviors are triggered by tracking temporal tactile data to detect slip [5], [34]. A grasping force is applied to counteract the perturbing force that results from object manipulation by the robot [8], and force feedback control is used for stabilizing the grasps during explicit finger repositioning for object rotation and translation behaviors [3].…”

Section: B Reactive Grasping and Contact Maintenancementioning

confidence: 99%

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Iterative learning of grasp adaptation through human corrections

Sauser

Argall

Metta

et al. 2012

Robotics and Autonomous Systems

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Abstract-In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work we introduce an approach for grasp adaptation which learns a statistical model to adapt hand posture solely based on the perceived contact between the object and fingers. Using a multi-step learning procedure, the model dataset is built by first demonstrating an initial hand posture, which is then physically corrected by a human teacher pressing on the fingertips, exploiting compliance in the robot hand. The learner then replays the resulting sequence of hand postures, to generate a dataset of posture-contact pairs that are not influenced by the touch of the teacher. A key feature of this work is that the learned model may be further refined by repeating the correction-replay steps. Alternatively, the model may be reused in the development of new models, characterized by the contact signatures of a different object. Our approach is empirically validated on the iCub robot. We demonstrate grasp adaptation in response to changes in contact, and show successful model reuse and improved adaptation with additional rounds of model refinement.

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Related Work

Pfanne

2022

Springer Tracts in Advanced Robotics

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Adaptive grasping by multi fingered hand with tactile sensor based on robust force and position control

Cited by 57 publications

References 13 publications

Dexterous grasping under shape uncertainty

Dexterous grasping under shape uncertainty

Iterative learning of grasp adaptation through human corrections

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