Skills for physical artificial intelligence

Miriyev, Aslan; Kovač, Mirko

doi:10.1038/s42256-020-00258-y

Cited by 47 publications

(41 citation statements)

References 17 publications

(15 reference statements)

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“…Geckos run with ease across water (Nirody et al, 2018), crocodiles roll in complex patterns to kill their prey (Fish et al, 2007), and despite continually changing flow conditions and strong locomotor requirements, fish may travel upstream for weeks while fasting (Crossin et al, 2004). Animals outperform robotic platforms and are more resilient than traditional robots in large part because of their compliant structures with integrated sensing capabilities, which enable them to respond to unexpected changes and improve stability through morphological intelligence (Woodward and Sitti, 2018;Siddall et al, 2019;Miriyev and Kovač, 2020;Shield et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry

et al. 2022

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Due to the difficulty of manipulating muscle activation in live, freely swimming fish, a thorough examination of the body kinematics, propulsive performance, and muscle activity patterns in fish during undulatory swimming motion has not been conducted. We propose to use soft robotic model animals as experimental platforms to address biomechanics questions and acquire understanding into subcarangiform fish swimming behavior. We extend previous research on a bio-inspired soft robotic fish equipped with two pneumatic actuators and soft strain sensors to investigate swimming performance in undulation frequencies between 0.3 and 0.7 Hz and flow rates ranging from 0 to 20 cms in a recirculating flow tank. We demonstrate the potential of eutectic gallium–indium (eGaIn) sensors to measure the lateral deflection of a robotic fish in real time, a controller that is able to keep a constant undulatory amplitude in varying flow conditions, as well as using Particle Image Velocimetry (PIV) to characterizing swimming performance across a range of flow speeds and give a qualitative measurement of thrust force exerted by the physical platform without the need of externally attached force sensors. A detailed wake structure was then analyzed with Dynamic Mode Decomposition (DMD) to highlight different wave modes present in the robot’s swimming motion and provide insights into the efficiency of the robotic swimmer. In the future, we anticipate 3D-PIV with DMD serving as a global framework for comparing the performance of diverse bio-inspired swimming robots against a variety of swimming animals.

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Section: Introductionmentioning

confidence: 99%

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry

et al. 2022

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“…The animal kingdom is an endless source of inspiration for soft robotics 1 , 2 . Researchers have constructed compliant robots that can mimic all kinds of animal motions, like octopus locomotion 3 , elephant trunk grasping 4 , insect flying 5 , jellyfish and fish swimming 6 – 8 , as well as snake and insects crawling 9 – 11 .…”

Section: Introductionmentioning

confidence: 99%

Physical reservoir computing with origami and its application to robotic crawling

Bhovad

2021

Sci Rep

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A new paradigm called physical reservoir computing has recently emerged, where the nonlinear dynamics of high-dimensional and fixed physical systems are harnessed as a computational resource to achieve complex tasks. Via extensive simulations based on a dynamic truss-frame model, this study shows that an origami structure can perform as a dynamic reservoir with sufficient computing power to emulate high-order nonlinear systems, generate stable limit cycles, and modulate outputs according to dynamic inputs. This study also uncovers the linkages between the origami reservoir’s physical designs and its computing power, offering a guideline to optimize the computing performance. Comprehensive parametric studies show that selecting optimal feedback crease distribution and fine-tuning the underlying origami folding designs are the most effective approach to improve computing performance. Furthermore, this study shows how origami’s physical reservoir computing power can apply to soft robotic control problems by a case study of earthworm-like peristaltic crawling without traditional controllers. These results can pave the way for origami-based robots with embodied mechanical intelligence.

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“…With the concept of learning, we gather together the complex system of a distributed network of neuromorphic interfaces along the robotic body or fully endowed at the end‐effector tip, and the computation mechanisms, that should be able to acquire, analyze, recognize, and process data and patterns. [ 66 ] Thus, the learning and recognition intelligence should act as an encoder of the physical intelligence [ 67 ] (hereafter an interchangeable term to label the combination of mechanical and material intelligence). This challenging achievement should drive toward proprioceptive‐ and embodied intelligence‐based innovative machines that can mediate responses to external stimuli by sensorial capacities, or, e.g., optically and mechanically reconfigure to replicate camouflage as in the biotic counterpart.…”

Section: Introductionmentioning

confidence: 99%

A Perspective on Cephalopods Mimicry and Bioinspired Technologies toward Proprioceptive Autonomous Soft Robots

Giordano

Carlotti

Mazzolai

2021

Adv Materials Technologies

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scientific community. [1][2][3][4][5][6][7] Regardless, every time a living being reveals novel adaptive and dynamically reactive mimicry behavior, it inspires and fosters futuristic and unexpected technological outcomes. [8][9][10][11][12] At the biological level, the visual crypsis is the ability of a species to resemble the surroundings by matching the coloration and the geometrical patterns of the habitat. In this sense, a living being can control its appearance optically (thanks to arranged and optimized structures at the mesoscopic scale, by means of pigmentation, or emissive elements) and morphologically (it can show wrinkles and textures on the body to evade detection or observation). [13][14][15][16][17][18] Both these mechanisms are characterized by a time response that ranges from milliseconds to hundreds of seconds. In nature, several species take advantage of cryptic abilities, as, e.g., in cephalopods, [7] crustaceans, [19] reptilians, [1,20,21] insects, [22,23] birds, [24,25] shells, [26,27] plants, [28,29] among many. The biotic color changing and body patterning relate to reproductive, communicative, and defensive and/or predatory strategies. Unfortunately, the nervous or central control-chain system that steers these behaviors in animals and plants, is still somehow fogged for scientists. [7,[30][31][32] A complete knowledge about their central information process systems, can open to astonishing developments in many scientific branches, from neurobiology [33,34] to quantum biology. [35] Undoubtedly, the most debated case of study in the natural world are cephalopods, which not only are highly evolved and specialized in fast adaptive color changing displays, but also are able to actuate their skin to generate 3D patterns, when exposed to specific mechanical, thermal, optical, or chemical stimuli. Soft muscular arrangements, [36][37][38] spatially distributed and expandable absorbing components (namely chromatophores), [39,40] iridescent elements (namely irido phores), [41,42] and bright white scatterers (namely leucophores) [43] are responsible for textural, postural and color changing. Moreover, octopuses are a remarkable intelligent species, able, for example, to open a jar or avoid predators in the order of seconds. [44] Thus, cephalopods are usually regarded as a perfect example of embodied intelligence, [45] thanks to the symbiosis between their body's mechanics and morphology, and the distributed sensory neuro-motor-control system. Their "learning", "mechanical" and "material intelligence" will be our lodestars along this review, resulting in a fascinating connection between Octopus skin is an amazing source of inspiration for bioinspired sensors, actuators and control solutions in soft robotics. Soft organic materials, biomacromolecules and protein ingredients in octopus skin combined with a distributed intelligence, result in adaptive displays that can control emerging optical behavior, and 3D surface textures with rough geometries, with a remarkably high control speed (≈ms). To be able ...

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Skills for physical artificial intelligence

Cited by 47 publications

References 17 publications

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry

Physical reservoir computing with origami and its application to robotic crawling

A Perspective on Cephalopods Mimicry and Bioinspired Technologies toward Proprioceptive Autonomous Soft Robots

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