Control of Tendon-Driven Soft Foam Robot Hands

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

doi:10.1109/humanoids.2018.8624937

Cited by 34 publications

(13 citation statements)

References 20 publications

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“…Similarly, we might also allow for a more interactive placement of tendon routing points or provide a set of suggestions from which the user can choose a preferred tendon layout for transmission. Numerical simulation tools, such as finite element methods, can be used to describe the micro and macroscopic deformation behaviour of foam-bodied soft robots [31], [32]. We plan to use such simulations in future work to inform and improve our soft robot design process.…”

Section: Discussion Conclusion and Future Workmentioning

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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Section: Discussion Conclusion and Future Workmentioning

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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“…Additionally, George Thuruthel et al (2018) mention nonlinear material effects such as compliance, visco-elastic material behaviors, and hysteresis, as well as the wide range of design and actuation techniques that account for the non-trivial nature of this problem. Previous works have particularly studied the problem of inverse kinematics (IK) which is concerned with finding a mapping between actuator configuration and desired hand configuration (i.e., pose) (Rolf and Steil, 2013;George Thuruthel et al, 2016;Jiang et al, 2017;Schlagenhauf et al, 2018;Bauer et al, 2020). Existing control approaches can be classified into three main categories: model-based or model-free controllers, as well as combinations of both.…”

Section: Related Workmentioning

confidence: 99%

Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands

2021

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There has been an explosion of ideas in soft robotics over the past decade, resulting in unprecedented opportunities for end effector design. Soft robot hands offer benefits of low-cost, compliance, and customized design, with the promise of dexterity and robustness. The space of opportunities is vast and exciting. However, new tools are needed to understand the capabilities of such manipulators and to facilitate manipulation planning with soft manipulators that exhibit free-form deformations. To address this challenge, we introduce a sampling based approach to discover and model continuous families of manipulations for soft robot hands. We give an overview of the soft foam robots in production in our lab and describe novel algorithms developed to characterize manipulation families for such robots. Our approach consists of sampling a space of manipulation actions, constructing Gaussian Mixture Model representations covering successful regions, and refining the results to create continuous successful regions representing the manipulation family. The space of manipulation actions is very high dimensional; we consider models with and without dimensionality reduction and provide a rigorous approach to compare models across different dimensions by comparing coverage of an unbiased test dataset in the full dimensional parameter space. Results show that some dimensionality reduction is typically useful in populating the models, but without our technique, the amount of dimensionality reduction to use is difficult to predict ahead of time and can depend on the hand and task. The models we produce can be used to plan and carry out successful, robust manipulation actions and to compare competing robot hand designs.

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“…However, working with such a hand requires the application of new modelling and control techniques. Schlagenhauf et al [252] provided users with tools and strategies to create and control dexterous foam robot hands. The primary aim of this work is to evaluate and compare different control strategies for solving the inverse kinematics problem of foam robots.…”

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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Control of Tendon-Driven Soft Foam Robot Hands

Cited by 34 publications

References 20 publications

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

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

Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands

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

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