Human to Robot Hand Motion Mapping Methods: Review and Classification

Meattini, Roberto; Suárez, Raúl; Palli, Gianluca; Melchiorri, Claudio

doi:10.1109/tro.2022.3205510

Cited by 2 publications

(2 citation statements)

References 135 publications

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“…Compared to artificial hands, there are differences in movement, force, and sensory responses [129]. As a result, artificial hands need to be able to learn and adjust their movements based on experience and specific tasks [130,131].…”

Section: Control Synergiesmentioning

confidence: 99%

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Gentile,

Gruppioni

2023

Prosthesis

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The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user’s intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand.

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Section: Control Synergiesmentioning

confidence: 99%

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Gentile,

Gruppioni

2023

Prosthesis

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“…There are various methods for providing the required data for dexterous manipulation based on employing various sensing and data collection techniques. Optical tracking systems [43], magnetic sensors [44], force and tactile sensors [8], and glove-based sensors [45] are the most common approaches. Human-to-robot hand motion mapping [46,47] is frequently required in several applications, and the approaches to address this problem fall under the following six categories [45]: direct joint, direct Cartesian, task-oriented, dimensionality reduction-based, pose recognition-based, and hybrid mappings.…”

mentioning

confidence: 99%

Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

Sheikhsamad,

Suárez,

Rosell

2024

Machines

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Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper.

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Human to Robot Hand Motion Mapping Methods: Review and Classification

Cited by 2 publications

References 135 publications

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

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