Robust object manipulation for tactile-based blind grasping

Cortez, Wenceslao Shaw; Oetomo, Denny; Manzie, Chris; Choong, Peter

doi:10.1016/j.conengprac.2019.104136

Cited by 13 publications

(9 citation statements)

References 49 publications

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“…It significantly reduced the grasping gestures that needed to be decoded via sEMG. According to tactile-based blind grasping, the robust control law tends to use all known fingers to perceive unknown objects, thus completing the power grasp (Shaw-Cortez et al, 2019 ). By selecting one sEMG enabling flag corresponding to the blind power grasping with all fingers (Shaw-Cortez et al, 2019 ), another sEMG-based detailed decoding can be left to precision grasp gesture (such as pinch), gestures with specific usage (such as poke) and other sign languages.…”

Section: Methodsmentioning

confidence: 99%

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

Zhang

Lu²,

Fan³

et al. 2022

Front. Neurorobot.

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This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support Vector Machine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability (p < 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150 ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%.

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Section: Methodsmentioning

confidence: 99%

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

Zhang

Lu²,

Fan³

et al. 2022

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…Going a step further to kinematic constraints, a dynamical closed loop controller was developed in [ 14 , 15 , 16 ]—where the only knowledge needed was sensing of finger−robot states and kinematic parameters in a finger model. This approach was called blind grasping and has spawned more research in recent years [ 17 ], even with tackling challenges such as unknown object mass, shape, Coriolis terms and hand/object kinematics [ 18 ]. Although it is desirable to perform manipulation with as little information as possible as reasoned in [ 15 ], it is advantageous in practical situations to choose to use the information which is readily available—robot hand geometry, robot parameters, etc.…”

Section: Approaches To Dexterous Robotic Manipulationmentioning

confidence: 99%

Towards Haptic-Based Dual-Arm Manipulation

Turlapati

Campolo

2022

Sensors

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Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand−object pose tracking faces challenges such as occlusions and objects moving out of view during robotic manipulation. In this work, we show that object kinematics can be inferred from local haptic feedback at the robot−object contact points, combined with robot kinematics information given an initial vision estimate of the object pose. A planar, dual-arm, teleoperated robotic setup was built to manipulate an object with hands shaped like circular discs. The robot hands were built with rubber cladding to allow for rolling contact without slipping. During stable grasping by the dual arm robot, under quasi-static conditions, the surface of the robot hand and object at the contact interface is defined by local geometric constraints. This allows one to define a relation between object orientation and robot hand orientation. With rolling contact, the displacement of the contact point on the object surface and the hand surface must be equal and opposite. This information, coupled with robot kinematics, allows one to compute the displacement of the object from its initial location. The mathematical formulation of the geometric constraints between robot hand and object is detailed. This is followed by the methodology in acquiring data from experiments to compute object kinematics. The sensors used in the experiments, along with calibration procedures, are presented before computing the object kinematics from recorded haptic feedback. Results comparing object kinematics obtained purely from vision and from haptics are presented to validate our method, along with the future ideas for perception via haptic manipulation.

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“…Haptics is one of the important sensing modalities used to perceive object physical properties, surface properties, and interaction forces between the end-effectors and the objects ( Shaw Cortez et al, 2019 ; Scimeca et al, 2020 ; Mayer et al, 2020 ). Object identification is one of the most important applications of haptics, particularly, in cases where the identification process needs to rely on the information provided only through the physical interaction between the end-effector and the objects, or when it cannot be conveniently achieved by other means ( Dargahi and Najarian, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

On the Efficiency of Haptic Based Object Identification: Determining Where to Grasp to Get the Most Distinguishing Information

et al. 2021

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Haptic perception is one of the key modalities in obtaining physical information of objects and in object identification. Most existing literature focused on improving the accuracy of identification algorithms with less attention paid to the efficiency. This work aims to investigate the efficiency of haptic object identification to reduce the number of grasps required to correctly identify an object out of a given object set. Thus, in a case where multiple grasps are required to characterise an object, the proposed algorithm seeks to determine where the next grasp should be on the object to obtain the most amount of distinguishing information. As such, the paper proposes the construction of the object description that preserves the association of the spatial information and the haptic information on the object. A clustering technique is employed both to construct the description of the object in a data set and for the identification process. An information gain (IG) based method is then employed to determine which pose would yield the most distinguishing information among the remaining possible candidates in the object set to improve the efficiency of the identification process. This proposed algorithm is validated experimentally. A Reflex TakkTile robotic hand with integrated joint displacement and tactile sensors is used to perform both the data collection for the dataset and the object identification procedure. The proposed IG approach was found to require a significantly lower number of grasps to identify the objects compared to a baseline approach where the decision was made by random choice of grasps.

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Robust object manipulation for tactile-based blind grasping

Cited by 13 publications

References 49 publications

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

Towards Haptic-Based Dual-Arm Manipulation

On the Efficiency of Haptic Based Object Identification: Determining Where to Grasp to Get the Most Distinguishing Information

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