Grasp mapping for Dexterous Robot Hand: A hybrid approach

Chattaraj, Ritwik; Bepari, Bikash; Bhaumik, Subhasis

doi:10.1109/ic3.2014.6897180

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…Fingertip mapping is applicable to precision grasps and joint mapping is applicable to power grasps [4]. Our experiments confirm this holds true for novice users.…”

Section: Discussionsupporting

confidence: 74%

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Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace

Meeker

Rasmussen

Ciocarlie

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Human-in-the-loop manipulation is useful in when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator's hand as an input device can provide an intuitive control method but requires mapping between pose spaces which may not be similar. We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We present an algorithm to project between pose space and teleoperation subspace. We use a non-anthropomorphic robot to experimentally prove that it is possible for teleoperation subspaces to effectively and intuitively enable teleoperation. In experiments, novice users completed pick and place tasks significantly faster using teleoperation subspace mapping than they did using state of the art teleoperation methods.

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“…Fingertip mapping is applicable to precision grasps and joint mapping is applicable to power grasps [4]. Our experiments confirm this holds true for novice users.…”

Section: Discussionsupporting

confidence: 74%

“…If the human and robot joints have a clear correspondence, the human joint angles can be imposed directly onto the robot joints with little or no transformation [3]. This mapping is most useful for power grasps [4], and is limited if the robot hand is non-anthropomorphic.…”

Section: Related Workmentioning

confidence: 99%

Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace

Meeker

Rasmussen

Ciocarlie

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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show abstract

“…If the human and robot joints have a clear correspondence, the human joint angles can be imposed directly onto the robot joints with little or no transformation [3]. This mapping is most useful for power grasps [7], and is limited if the robot hand is non-anthropomorphic.…”

Section: Related Workmentioning

confidence: 99%

“…These undergo scaling to find the desired robot Cartesian fingertip positions and then inverse kinematics determine robot joint angles. This mapping is useful for precision grasps [7].…”

Section: Related Workmentioning

confidence: 99%

A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands

Meeker¹,

Haas-Heger²,

Ciocarlie³

2019

Preprint

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Teleoperation is a valuable tool for robotic manipulators in highly unstructured environments. However, finding an intuitive mapping between a human hand and a nonanthropomorphic robot hand can be difficult, due to the hands' dissimilar kinematics. In this paper, we seek to create a mapping between the human hand and a fully actuated, nonanthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users. To accomplish this, we propose a low-dimensional teleoperation subspace which can be used as an intermediary for mapping between hand pose spaces. We present two different methods to define the teleoperation subspace: an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and an algorithmic definition, which is kinematically independent, and uses objects to define the subspace. We use each of these definitions to create a teleoperation mapping for different hands. We validate both the empirical and algorithmic mappings with teleoperation experiments controlled by novices and performed on two kinematically distinct hands. The experiments show that the proposed subspace is relevant to teleoperation, intuitive enough to enable control by novices, and can generalize to nonanthropomorphic hands with different kinematic configurations.

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“…However, controlling an arm with several DOFs is a difficult task in which the accuracy of motion of all joints needs to be guaranteed and their trajectories need to be pre-planned according to the requirements of the given task (Shimoga, 1996 ; Bicchi and Kumar, 2000 ). For an industrial manipulator, an inverse kinematic technique is used to compute robotic joint motions, but this method requires a large amount of calculation (Chattaraj et al, 2014 ). In prosthetics, several grasping modes are planned in advance, the operator's intention is identified, and finite-state machines (FSM) or pattern recognition (PR) methods are used to switch between modes (Purushothaman, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

et al. 2021

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The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.

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Grasp mapping for Dexterous Robot Hand: A hybrid approach

Cited by 13 publications

References 8 publications

Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace

Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace

A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

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