A soft robot that adapts to environments through shape change

Shah, Dylan; Powers, Joshua; Tilton, Liana G.; Kriegman, Sam; Bongard, Josh; Kramer‐Bottiglio, Rebecca

doi:10.1038/s42256-020-00263-1

Cited by 123 publications

(94 citation statements)

References 51 publications

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“…Somewhat surprisingly, economic theory provided one of the first intuition pumps for considering the nonhuman generators of machines: machines arise from the literal hands of human engineers but also the "invisible hand" of a free market; the latter set of pressures in effect "select, " without human design or forethought, which technologies proliferate (Beinhocker, 2020). More recently, evolutionary algorithms, a type of machine learning algorithm, have demonstrated that, among other things, jet engines (Yu et al, 2019), metamaterials (Zhang et al, 2020), consumer products (Zhou et al, 2020), robots (Brodbeck et al, 2018;Shah et al, 2020), and synthetic organisms (Kriegman et al, 2020) can be evolved rather than designed: an evolutionary algorithm generates a population of random artifacts, scores them against human-formulated desiderata, and replaces low-scoring individuals with randomly modified copies of the survivors. Indeed, the "middle man" has even been removed in some evolutionary algorithms by searching for novelty rather than selecting for a desired behavior (Lehman and Stanley, 2011).…”

Section: Machines Are Designed By Humans: Life Is Evolvedmentioning

confidence: 99%

“…However, another, operational interpretation of their etymology is possible: it is harder to change hardware than software, but examples abound of both, radical structural change (Birnbaum and Alvarado, 2008;Levin, 2020a) and learning/plasticity at the dynamical system level that does not require rewiring (Biswas et al, 2021). Programmable matter (Hawkes et al, 2010) and shape changing soft robots (Shah et al, 2020) are but two technological disciplines investigating physically fluid technologies. In one study the assumption that changing hardware is hard was fully inverted: it was shown that a soft robot may recover from unexpected physical injury faster if it contorts its body into a new shape (a hardware change) rather than learning a compensating gait [a software change; (Kriegman et al, 2019)].…”

Section: Software/hardwarementioning

confidence: 99%

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Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

Bongard

Levin

2021

Front. Ecol. Evol.

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One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

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Section: Machines Are Designed By Humans: Life Is Evolvedmentioning

confidence: 99%

Section: Software/hardwarementioning

confidence: 99%

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

Bongard

Levin

2021

Front. Ecol. Evol.

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Section: Shape Changing Robotsmentioning

confidence: 99%

“…[43] Another simulated shape changing robot used an inflatable core to transition between a cylindrical shape and a flattened sheet-like shape to adapt its locomotion to different environments. [87] Initially a rolling cylinder on flat terrain, the robot changed to the flattened shape with an inchworm gait to maintain efficient locomotion up an incline. This simulated robot design was successfully transferred to reality, thereby realizing a physical robot that utilizes shape change to gain access to additional environments (Figure 3b).…”

Section: Shape Changing Robotsmentioning

confidence: 99%

“…The application of evolutionary algorithms to simulated robots has begun to address the questions above, but only within empirical studies with one or two objectives for the robots to solve. [31,87,104] Further, higher-level and/or transenvironmental tasks have been largely unexplored. Although evolved simulated robots have transitioned between terrestrial and aqueous environments, [64] no general understanding of how shape change can equip a machine to travel through air, water, or over land yet exists.…”

Section: Shape Findingmentioning

confidence: 99%

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Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

et al. 2020

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are specialized for a single task, and cannot adapt their body to accomplish additional tasks after manufacture. Moreover, biological bodies are often highly regenerative, and able to repair and reconfigure their large-scale architecture in the face of significant damage or radical changes to their components. [3] For example, salamanders regenerate amputated limbs, [4] and fragments cut from arbitrary portions of planaria flatworms can rebuild (and rescale) their bodies to recover a full, correct anatomy. [3] Remarkably, many of these systems are able to retain information, such as learned memories, despite drastic reconfiguration or total replacement of their brains. [5] In these integrated living systems, intelligence, memory, learning, behavior, and body structure are all intertwined and emerge from the multiscale dynamics of the same robust and highly fault-tolerant medium.Evolution did not result in hard-coded body plans purely determined by genetic factors, but rather produced diverse examples of intelligent self-modifying systems which adapt to numerous extragenomic influences. [6] In this way, biology serves as an important proof-of-principle, and design challenge, for artificial intelligence and shape changing robots. Despite having access to this extensive set of model systems, the realization of general-purpose, adaptive robots has remained elusive. Researchers have proposed modular robots that can be attached to each other to expand functionality, [7] passively conforming universal grippers, [8] reconfigurable robotic skins, [9] self-assembling robot swarms, [10] gait-switching mechanisms [11] and controllers, [12,13] and algorithms that quickly re-adapt to multiple distinct tasks. [14] Such approaches succeed at adaptation but operate under the assumption that the robot's body is only reconfigured or reshaped due to external forces, and do not explore the possibility of synthetic machines that actively grow, regenerate, deform, or otherwise change the resting shape of their constituent components.With the introduction of a conformable gripper by Hirose in 1978, [15] followed by continuum robot arms, [16] silicone grippers, [17] and variable stiffness actuators, [18] robots that can adapt to real-world environments by changing their shape are becoming closer to reality. In particular, the idea of passively conforming around objects during grasping has been quite successful. [17,19,20] Soft robots have shown potential in other applications, including human-robot interaction and exploration, as reviewed by Kim et al., [21] Rus et al., [22] and others. [23,24] For a comprehensive review of the role of deformation in singlefunction soft robots, the reader is referred to Wang et al. [25] One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for a...

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Rigidity‐Tunable Materials for Soft Engineering Systems

Roh,

Lim,

Kang

et al. 2024

Adv Eng Mater

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Engineering systems that leverage the flexibility and softness of soft materials have been fostering revolutionary progress and broad interest across various applications. The inherently flexible mechanical properties of these materials lay the groundwork for engineering systems that can adapt comparably to biological organisms, enabling them to adjust to unpredictable environments effectively. However, alongside the positive benefits of softness, these systems face challenges such as low durability, continuous energy demands, and compromised task performance due to the inherently low stiffness of soft materials. These limitations pose significant obstacles to the practical impact of soft engineering systems in the real world beyond innovative concepts. This review presents a strategy that employs materials with variable stiffness to balance adaptability advantages with the challenge of low rigidity. We summarize developments in materials capable of stiffness modulation alongside their applications in electronics, robotics, and biomedical fields. This focus on stiffness modulation at the material unit level is a critical step toward enabling the practical application of soft engineering systems in real‐world scenarios.This article is protected by copyright. All rights reserved.

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A soft robot that adapts to environments through shape change

Cited by 123 publications

References 51 publications

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

Rigidity‐Tunable Materials for Soft Engineering Systems

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