Toward next-generation learned robot manipulation

Cui, Jinda; Trinkle, Jeff

doi:10.1126/scirobotics.abd9461

Cited by 56 publications

(17 citation statements)

References 88 publications

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“…The dynamic tactile perception is to sense the external environment stimuli just like the soft biological tissues such as the elephant trunk or the octopus' tentacles. It is still a great challenge, but is essential for a soft manipulator to interact with its surroundings (Cui & Trinkle, 2021; Man & Damasio, 2019). In the primary stage, the hard tactile sensors, such as the force/torque sensor and the force sensitive resistance sensor, are tried to conduct the tactile perception.…”

Section: Enabling Technologies Of the Soft Manipulatormentioning

confidence: 99%

A review of soft manipulator research, applications, and opportunities

Chen

Zhang

Huang

et al. 2021

Journal of Field Robotics

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Soft manipulator is a kind of special manipulator that uses soft material or flexible structure to perform manipulation task under the specific drive mode inspired by the soft tissue. It is developing gradually as an important component of the soft robot. In this paper, the typical design cases of the soft manipulators as well as the related research teams are firstly investigated. The characteristics of different drive modes are compared and analyzed. Based on these investigations, the enabling technologies are systematically summarized in five aspects, such as kinematic and dynamic modeling, motion control, shape detection, dynamic tactile perception, and stiffening method. Currently, the research on soft manipulator has initially formed a technical system, although it is still in the primary stage. With the development of the soft manipulator, its overall technology maturity will be gradually improved. Currently, the applications of the soft manipulator lie mainly in the fields of the ground rescue, the underwater grabbing, the medical operation, the assist maintenance, the man–machine interaction, the space manipulation and so on. The application fields, such as origami soft manipulator, biomimetic soft microrobots, self‐healing soft robot, space soft manipulator and so on, emerge new opportunities and challenges for the soft manipulators.

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Section: Enabling Technologies Of the Soft Manipulatormentioning

confidence: 99%

A review of soft manipulator research, applications, and opportunities

Chen

Zhang

Huang

et al. 2021

Journal of Field Robotics

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“…There are already several works reviewing the robot manipulation domain (Billard, 2019 ; Cui and Trinkle, 2021 ), reinforcement learning for the robot (Hua et al, 2021 ; Zhang and Mo, 2021 ), and dexterous manipulation only (Prattichizzo et al, 2020 ). However, as far as we know, a survey focusing on dexterous manipulation with multi-fingered robotic hands with reinforcement learning has never been presented before.…”

Section: Introductionmentioning

confidence: 99%

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

Wang

2022

Front. Neurorobot.

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With the increasing demand for the dexterity of robotic operation, dexterous manipulation of multi-fingered robotic hands with reinforcement learning is an interesting subject in the field of robotics research. Our purpose is to present a comprehensive review of the techniques for dexterous manipulation with multi-fingered robotic hands, such as the model-based approach without learning in early years, and the latest research and methodologies focused on the method based on reinforcement learning and its variations. This work attempts to summarize the evolution and the state of the art in this field and provide a summary of the current challenges and future directions in a way that allows future researchers to understand this field.

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“…From the learning and control aspect, due to the high dimensionality of states and action spaces, frequently switched interaction modes between multifingered hands and objects, making the direct using of typical analytic methods and learning from scratch methods for two-fingered grippers arduous. Moreover, from the application aspect, the multifingered robotic hand and its algorithms are always personalized, which will limit their transfer and adaptation for widespread applications, as the "internal adaptation" for the body, software, and perception variations, and "external adaptation" for the environment, object, and task variations are intractable (Cui and Trinkle, 2021 ). Along with the related studies, benchmark experimental environments and tasks to fairly and comprehensively compare and evaluate the various properties, such as precision, efficiency, robustness, safety, success rate, adaptation, are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

“…By reviewing these correlative studies, especially their cross-over studies, we hope to give some insights into the design, learning, and control of multifingered dexterous hands. Note that there are some reviews concerning the structure, sensors of robotic hands, and control and learning for robotic grasping, assembly, and manipulation (Bicchi, 2000 ; Yousef et al, 2011 ; Mattar, 2013 ; Controzzi et al, 2014 ; Ozawa and Tahara, 2017 ; Bing et al, 2018 ; Billard and Kragic, 2019 ; Kroemer et al, 2019 ; Li and Qiao, 2019 ; Mohammed et al, 2020 ; Cui and Trinkle, 2021 ; Qiao et al, 2021 ), while none of them unfold from these three aspects for multifingered dexterous hands with no less than three fingers (refer to Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Wang

et al. 2022

Front. Neurorobot.

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Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

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Toward next-generation learned robot manipulation

Cited by 56 publications

References 88 publications

A review of soft manipulator research, applications, and opportunities

A review of soft manipulator research, applications, and opportunities

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

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