Design. Fabrication, and Evaluation of Tendon-Driven Multi-Fingered Foam Hands

King, Jonathan P.; Bauer, Dominik; Schlagenhauf, Cornelia; Chang, Kai-Hung; Moro, Daniele; Pollard, Nancy S.; Coros, Stelian

doi:10.1109/humanoids.2018.8624997

Cited by 32 publications

(16 citation statements)

References 18 publications

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“…The authors aim to generate a multi-fine robotic hand grasping trajectory and obtain the gripping configuration from objects with a known pose by optimizing the trajectory. Furthermore, research has shown that the softness and flexibility of foam robots provide a great advantage in secure grasping and robust in-hand manipulation [251]. However, working with such a hand requires the application of new modelling and control techniques.…”

Section: Vision-based Robotic Graspmentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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Section: Vision-based Robotic Graspmentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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“…Different designs and actuation methods like shape memory alloys (SMA) driven actuators [64], tendon driven actuators [65], Fluid driven actuator [66] and pneumatic actuators [67] has been developed for rehabilitation robotic gloves. Most of the tendon driven cables support only daily living activities and have limited output force and hyperextension.…”

Section: Introductionmentioning

confidence: 99%

“…SMA actuators have high operating temperatures ranging from (100°C-500°C). Complex design of SMA actuators made it difficult to use in rehabilitation purposes and daily living activities [64][65][66][67][68]. Pneumatic actuators were selected due to higher stiffness, low weight and simpler design as compared to above mentioned actuators.…”

Section: Introductionmentioning

confidence: 99%

Low Cost Soft Robotic Gloves for At-home Rehabilitation and Daily Living Activities

Souhail

Vessakosol

2019

JAMRIS

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Stroke is one of the major reasons which affect the human hand functionality and lead to disability. Different repetitive exercises are used to regain the hand functionality which involves robotic exoskeleton. Soft pneumatic actuators are one of the good alternatives to rigid and fixed exoskeletons for rehabilitation. This paper presents soft robotic gloves fabricated with two different lowcost silicones which can be used in daily living activities and rehabilitation purpose. Soft robotic gloves are light weight and compact. These robotic gloves utilize the pneumatic pressure to flex and extend the human hand. Soft robotic gloves were tested on a healthy object for grasping and rehabilitation ability. Results showed that robotic glove was able to grasping and do the Kapandji test. This work presents an important step toward low cost efficient soft robotic devices for rehabilitation of stroke patients.

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“…Poroelastic foams encapsulated in a PDMS (polydimethylsiloxane), or in a silicone elastomer sealing layer, have been used for fabricating soft fluidic actuators [17], [18], [19]. Previous work also investigated tendon-based actuation and transmission systems [20], [21], [22]. In [20], [22], tendons were sewed through a fabric "skin" placed around the soft robot in order to reduce friction.…”

mentioning

confidence: 99%

“…Previous work also investigated tendon-based actuation and transmission systems [20], [21], [22]. In [20], [22], tendons were sewed through a fabric "skin" placed around the soft robot in order to reduce friction. Both of these previous tendon and fluidic transmission approaches not only add an extra step to the fabrication process of the soft robot, but also introduce associated uncertainties and failure risks.…”

mentioning

confidence: 99%

Expanding Foam as the Material for Fabrication, Prototyping and Experimental Assessment of Low-Cost Soft Robots With Embedded Sensing

Somm

Hahn

Kumar

et al. 2019

IEEE Robot. Autom. Lett.

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Fabricating robots from soft materials imposes major constraints on the integration and compatibility of embedded sensing, transmission, and actuation systems. Various soft materials present different challenges, but also new opportunities, for novel fabrication techniques, integrated soft sensors, and embedded actuators. For instance, extensive research on silicone elastomers has led to the development of soft sensors based on closed channels filled with liquid metal conductors, as well as corresponding fluidic actuators by pressurizing cavities within the body. In this paper, we present a novel approach to soft robot fabrication using soft expanding foam as the base material. While recent research points to elastic foams as a means to reduce material, manufacturing costs, and robot mass, they have not been explored much in the literature. This paper presents fabrication and prototyping techniques for developing low cost, custom-shaped soft robots from expanding polyurethane foam. We describe how to integrate user-defined routing points for transmission and actuation through cable-driven electrical actuation systems directly into the foam. Furthermore, we explore novel fabrication and prototyping techniques in order to build and integrate soft sensors into the foam substrate, which we demonstrate on soft robots varying in design complexity from a soft gripper to a soft "puppy".

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Design. Fabrication, and Evaluation of Tendon-Driven Multi-Fingered Foam Hands

Cited by 32 publications

References 18 publications

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

Low Cost Soft Robotic Gloves for At-home Rehabilitation and Daily Living Activities

Expanding Foam as the Material for Fabrication, Prototyping and Experimental Assessment of Low-Cost Soft Robots With Embedded Sensing

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