Computing with a muscular-hydrostat system

Nakajima, Kohei; Hauser, Helmut; Kang, Rongjie; Guglielmino, Emanuele; Caldwell, Darwin G.; Pfeifer, Rolf

doi:10.1109/icra.2013.6630770

Cited by 38 publications

(31 citation statements)

References 40 publications

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“…The parameters f 1 , f 2 , and f 3 are set to 2.11, 3.73, and 4.33, respectively. Similar inputs were adopted in Hauser et al (2011), Sumioka et al (2011), and Nakajima et al (2013). …”

Section: Methodsmentioning

confidence: 99%

“…This suggests that the body of the octopus arm is highly involved in the production of movements. Accordingly, in robotics, there have been several attempts to characterize the role of the muscular-hydrostat system in terms of its anatomical structure (Mazzolai et al, 2007; Laschi et al, 2009, 2012; Vavourakis et al, 2012a,b) and functionality (Nakajima et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

“…Recently, it has been shown that non-linear mass-spring networks can be used as a computational resource (Caluwaerts and Schrauwen, 2011; Hauser et al, 2011, 2012; Sumioka et al, 2011; Caluwaerts et al, 2013; Nakajima et al, 2013). These works imply that the non-linear and elastic body dynamics of soft robots are not drawbacks for control, but rather can be directly exploited as a computational resource.…”

Section: Introductionmentioning

confidence: 99%

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A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm

Nakajima¹,

Hauser²,

Kang³

et al. 2013

Front. Comput. Neurosci.

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125

116

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The behaviors of the animals or embodied agents are characterized by the dynamic coupling between the brain, the body, and the environment. This implies that control, which is conventionally thought to be handled by the brain or a controller, can partially be outsourced to the physical body and the interaction with the environment. This idea has been demonstrated in a number of recently constructed robots, in particular from the field of “soft robotics”. Soft robots are made of a soft material introducing high-dimensionality, non-linearity, and elasticity, which often makes the robots difficult to control. Biological systems such as the octopus are mastering their complex bodies in highly sophisticated manners by capitalizing on their body dynamics. We will demonstrate that the structure of the octopus arm cannot only be exploited for generating behavior but also, in a sense, as a computational resource. By using a soft robotic arm inspired by the octopus we show in a number of experiments how control is partially incorporated into the physical arm's dynamics and how the arm's dynamics can be exploited to approximate non-linear dynamical systems and embed non-linear limit cycles. Future application scenarios as well as the implications of the results for the octopus biology are also discussed.

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“…The parameters f 1 , f 2 , and f 3 are set to 2.11, 3.73, and 4.33, respectively. Similar inputs were adopted in Hauser et al (2011), Sumioka et al (2011), and Nakajima et al (2013). …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm

Nakajima¹,

Hauser²,

Kang³

et al. 2013

Front. Comput. Neurosci.

Self Cite

125

116

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“…Recent works on morphological computational aspects for soft bodies [13]- [20] has shown that the mechanical properties of such infinite-dimensional bodies can be exploited as real-time computational assets. This approach is related to the reservoir computing framework [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Information Processing Capability of Soft Continuum Arms

Torres

Nakajima

Godage

2019

2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)

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Soft Continuum arms, such as trunk and tentacle robots, can be considered as the "dual" of traditional rigidbodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.

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“…In one case, a simple model of a human musculoskeletal system was used to identify the capacity of computation [9]. In a more biologically plausible example, the computational capacity of a muscular-hydrostat system was investigated and found to have a characteristic memory capacity [10]. In addition, such a system has been demonstrated to have the potential to emulate complex nonlinear dynamical systems, and closed-loop controls [11].…”

Section: Introductionmentioning

confidence: 99%

Spine dynamics as a computational resource in spine-driven quadruped locomotion

Zhao

Nakajima

Sumioka

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Recent results suggest that compliance and nonlinearity in physical bodies of soft robots may not be disadvantageous properties with respect to control, but rather of advantage. In the context of morphological computation one could see such complex structures as potential computational resources. In this study, we implement and exploit this view point in a spine-driven quadruped robot called Kitty by using its flexible spine as a computational resource. The spine is an actuated multi-joint structure consisting of a sequence of soft silicone blocks. Its complex dynamics are captured by a set of force sensors and used to construct a closed-loop to drive the motor commands. We use simple static, linear readout weights to combine the sensor values to generate multiple gait patterns (bounding, trotting, turning behavior). In addition, we demonstrate the robustness of the setup by applying strong external perturbations in form of additional loads. The system is able to fully recover to its nominal gait patterns (which are encoded in the linear readout weights) after the perturbation has vanished.1 Reservoir computing is a machine learning technique used to emulate complex, nonlinear computations by employing a randomly initiated (but afterwards fixed in their parameters) complex, nonlinear dynamical network of nonlinear dynamical systems (i.e., the reservoir). For more details we refer to [8].

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Computing with a muscular-hydrostat system

Cited by 38 publications

References 40 publications

A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm

A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm

Information Processing Capability of Soft Continuum Arms

Spine dynamics as a computational resource in spine-driven quadruped locomotion

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