This study aims to develop an autonomous decentralized motion generation system that adapts to the state of the target material to be mixed and transported within a peristaltic pump designed in a manner analogous to intestinal peristalsis. The authors investigate the discrepancy function, which serves as a control law for modular snake-and amoeba-type robots, and propose a method for achieving autonomous decentralized control by regulating the phases corresponding to the supply and exhaust states within each unit of the peristaltic pump. The application of the local feedback term g(θi) was experimentally verified using a two-unit coupled pump to investigate the transition from in-phase motion to mixed-phase motion. The results showed that the phase difference between the units transitioned from in-phase (0 rad) to anti-phase (π rad), demonstrating that the local feedback term induces a phase delay and enables the desired mixed motion. In addition, by applying a control law based on the discrepancy function to the material whose viscosity decreases as mixing progresses, the pump's motion pattern was observed to transition in response to the mixing state of the material. As a result, the motion pattern shifted from mixing motion to peristaltic motion approximately 230 s after the start of mixing. These findings indicate that the pump autonomously and in a decentralized manner switched its motion pattern in accordance with the internal state of the material.

This study presents a bistable tensegrity robot that can perform repetitive jumps using one motor. This robot is based on a tensegrity structure that uses rigid plate-shaped compressors. To achieve bistability in this structure, we optimized the position of additional springs using a physics simulator that considers geometric constraints attributed to the collision between compression materials. A prototype was constructed based on the simulation model. To achieve jumping repeatability, we used one motor to control three tendons, each used; to control the additional spring strain, trigger the snap-through motion, and reform the structure to its original form. The prototype could jump using snap-through motion and reform back to its original form based on motor rotation. Furthermore, the robot demonstrated its ability to jump over flights of stairs by attaching a stand with a slight angle and using jumping repeatability.

Origami/kirigami robotics are opening a path that leads to lightweight, compact, and expandable robots. However, it is generally challenging to design agile motions for origami/kirigami robots due to their size and the intrinsic limitation of the materials. In this paper, we propose to use the bistability of the waterbomb base structure to generate the swift motion of the robots. We evaluate the bistability of the waterbomb-based structure and build origami jumpers with different configurations of the body to help analyze the behavior of the waterbomb base bistable structure. The jumper is actuated by a phase change liquid pouch actuator. Our jumper is lightweight (0.3 g), flattenable, and able to jump to more than 12 times of its diameter and 112 times of its height.

This paper presents a novel method of realizing in-hand manipulation inspired by the peristaltic motion of a large-sized caterpillar. The sharp contrast between the proposed soft-bodied finger and the conventional hard/rigid robotic ones is peristaltic motion with compression and bending deformations. The design is based on the biological fact that large-size caterpillars (e.g., Bombyx mori) utilize bending and compression/extension of the body to produce crawling locomotion. Exploiting the multi-modal deformations, we demonstrated that the prototype hand comprising two proposed fingers could rotate and transport grasped objects. We also observed that the time gap of two-finger motion is required to stabilize in-hand manipulation. The design can provide new insights into designing a gripper inspired by soft-bodied creatures.

Although picking up objects a few centimeters in size is a common task, achieving such ability in a robot manipulator remains challenging. We take a step toward solving this problem by focusing on the task of picking a 1.0-cm screw from a bulk bin using only tactile information to achieve the task. Inspired by how humans pick up small objects from a bin, we propose a "grasp-separate" strategy for robotic picking, which involves grasping many objects first and then separating a single object through manipulation in the fingers, for robotic picking. Based on this strategy, we developed a tactile-based screw bin-picking system. We trained a convolution neural network to estimate the number of screws in the fingers first and built a controller that generates manipulation behaviors to separate a screw using reinforcement learning. To compensate for the low resolution of off-the-shelf tactile sensor arrays, we adopted active sensing, which uses observations obtained during a predefined simple movement. We show that this approach enhances the estimation accuracy and manipulation performance. Furthermore, to enable flexible finger motion, such as between the thumb and the index finger in a human hand, we propose a soft robot finger structure that leverages compliant materials. A soft actor-critic algorithm successfully found dexterous screw separation behaviors in compliant soft robotic fingers. In the evaluation, the system obtained an average success rate of 80%, which was difficult to achieve without the grasp-separate manipulation technique.

We used cells, which are the units that make up a living body, as building blocks to design a biomachine hybrid system and develop a tactile sensor that uses living cells as sensor receptors. We fabricated a novel cell tactile sensor with the electrodes formed using printed electronics technology. This sensor comprises elastic electrodes mounted on a soft material to acquire tactile information; similar to a conventional cell tactile sensor, it acquires signals through mechanical stimulation. Further, self-organization of cells can be induced, and logical processing such as selective responses to stimuli can be performed directly by the physical system, without any coding using programming languages. The proposed novel cell tactile sensor that uses printed electrodes is small enough to mount on robots. Interestingly, we confirmed the self-healing properties of the proposed sensor after cells were injured mechanically.

The exploration of spatially limited terrestrial or aquatic environments requires miniature and lightweight robots. Soft-bodied robot research is paving ways for a new class of small-scale robots that can navigate a variety of environments with minimum influence on the environment itself. However, it is generally challenging to design miniature soft-bodied robots that efficiently adapt to the change between viscous environments. A small-scale soft-bodied robot, which could slowly move on dry land, will need rapid motions to be able to swim in a wet environment. Although using snap-through buckling of a deformable body could help to create swift motions of the robot, merely applying the snap-through buckling does not improve the swimming speed of the robot so much. Here we propose a design of a stringy soft-bodied robot that can crawl on dry surfaces and swim in liquid environments. Besides taking advantage of the snap-through buckling using coil shape memory alloys (SMAs), we design the body of the robot with a geometrical overlapping of the active body segments and control the frequency of the undulation movement, which is crucial for the swimming locomotion. We evaluate the performance of the robot in different density and viscosity liquids such as cooking oil and Glycerin solution. We found that the robot needs to drastically change its undulation from low to high frequency when it moves from high to low viscosity environments. Our robot can swim at a speed of 3. 37 body-lengths per minute (BL/min) and crawl at a speed of 1. 74 BL/min. We anticipate our findings will help shed light on the design of soft-bodied robots that adapt to the changing environments efficiently.

Soft-bodied robots are getting attention from researchers as their potential in designing compliant and adaptive robots. However, soft-bodied robots also pose many challenges not only in non-linear controlling but also in design and fabrication. Especially, the non-compatibility between soft materials and rigid sensors/actuators makes it more difficult to design a fully compliant soft-bodied robot. In this paper, we propose an all-printed sensor and actuator for designing soft-bodied robots by printing silver nano-particle ink on top of a flexible plastic film. We can print bending sensors and thermal based actuators instantly with home-commodity inkjet printers without any pre/post-processing. We exemplify the application of this fabrication method with an all-printed paper caterpillar robots which can inch forward and sense its body bending angle.

Soft-bodied and continuum robots have shown great adaptability to the environment thanks to its flexibility of the body. They have great potential in environment exploring or rescuing mission. One of those robots is snake-like soft-bodied robots. A snake robot is often made by attaching passive wheels along a long body to achieve frictional anisotropy. This anisotropic structure helps to propel the body with serpentine locomotion and prevents it from sliding laterally. However, with a snake-like soft-bodied robot, attaching wheels is not only clumsy but also adding weight to the robot. In this paper, being inspired by the scales on the skin of a snake, we propose a designing scheme to achieve an all-printed wriggle soft-bodied robot by patterning high and low friction material to the ventral side of the robot. Compared to a totally flat ventral, we are able to speed-up the serpentine locomotion 2.8 times. Besides, by changing the configuration of high/low friction material, our wriggle soft-bodied robot can easily move forward or backward just by switching the controlling signal. The fabrication time is just less than 1 hour and the robot can achieve the speed of 26 mm/s.

This paper presents a methodology to design mechanosensor feedback to oscillator-based controller for worm-like soft-bodied robots. A reinforcement learning technique, i.e., PEPG, is employed to embed appropriate mechanosensor feedback to harness global entrainment among the controller, the body dynamics, and the environment without explicitly designing the interaction between the oscillators. Another reinforcement learning, actor-critic, was applied to train the controller for the simulation models to analyze the effectiveness of PEPG in the system. Furthermore, the gait controller was trained under different body dynamics, i.e., the physical model of a caterpillar and an earthworm. We found that PEPG is suitable for the system probably because it does not add exploration noise to actions and it conducts episode based parameter updates. The simulation results show the proposed method can acquire distinct behavior, i.e., caterpillars' crawling, inching and earthworms' crawling, under different body dynamics. The outcome implies, that by utilizing appropriate learning method, desired functionality can be achieved in soft-bodied robots without explicitly designing their behavior.

Building robots from soft materials provides opportunities to create more robust and adaptive designs: soft bodies can conform to complex shapes in the environment and they are able to cushion shocks and store elastic energy. The challenge, however, remains to control highly deformable moveable structures effectively. We have proposed that one useful approach is through an autonomous decentralized control using physically coupled mechano-sensory oscillators. We have developed a highly deformable 3-D printed soft robot (PS robot) as a platform to explore the validity of the control strategy. Based on this platform we introduce a caterpillar-like soft-bodied modular-robot that is controlled in a fully decentralized manner. This paper focuses on one of the advantageous characteristics of autonomous decentralized control, i.e., the extensibility/contractibility of the modularity structures. The numerical and experimental results demonstrate that simple oscillators (controllers) can interact with each other by deforming the soft-bodied structure leading to a phase gradient that propagates crawling locomotion. The PS robots provide a powerful platform for exploring this strategy and they can be generalized for use with different robot shapes and material properties.

Both postural maintenance and rhythm generation are keys to generating adaptive behavior in all animals. This is particularly evident in soft animals such as caterpillars, worm and flatworms that are capable of moving freely in all directions and adopting intricate postures. They can also exploit three-dimensional deformations and nonlinear structural properties to move in complex environments and to respond to external forces. These capabilities have inspired a new interest in using soft materials in robotic applications but highly deformable materials create significant design and control problems. In previous work the authors have developed a 3D-printed soft (3D-PS) robot, inspired by caterpillars, as a platform to investigate methods for controlling soft robots. The previous version of the robot is able to reproduce the different gait patterns (inching and crawling motion) of caterpillars by changing temporal difference in the rhythmic deformations of different body parts. In this paper, we have added posture control to the 3D-PS robot together with a steering capability. Experimental results show that although posture and steering are usually related, elastic and continuum properties of the soft body can produce more complex and versatile behaviors.

The underwater soft-robot inspired by polyclad flatworms has been developed. The oval, flat, soft body of the flatworm was represented by a rubber sheet. The sheet was controlled by controls with three degrees of freedom to allow flapping of both the lateral sides and the body axis. Swimming patterns, such as swimming forward, hovering, and swimming backwards, were achieved by coordinated movement of the lateral side flaps and the body axis of the soft robot.

Soft and continuum robots have the useful capability of adopting intricate postures and conforming to complex shapes. Furthermore, structures built from soft materials propagate mechanical energy from one part of the body to another, depending on its body shape, boundary condition, stiffness distribution, and so on. This makes the robots capable of producing a large number of force profiles to achieve useful behaviors and functionalities, even using a small number of actuators. Exploiting the soft mechanical property also enables to generate functional frictional forces, which is a key issue in controlling robot locomotion. In this paper, a highly deformable 3-D printed soft robot (PS robot) is presented, which is capable of generating complex, robust gaits on different inclines using a novel variable friction leg design. This design changes the frictional force depending on the robot posture and shape to facilitate robot locomotion. Shape memory alloy (SMA) coils are embedded into the robot in such a way that they act both as structural elements and actuators. This is the first soft robot platform produced by 3-D printing making fabrication simple and fast.

Behavioral diversity is an essential feature of living systems, enabling them to exhibit adaptive behavior in hostile and dynamically changing environments. However, traditional engineering approaches strive to avoid, or suppress, the behavioral diversity in artificial systems to achieve high performance in specific environments for given tasks. The goals of this research include understanding how living systems exhibit behavioral diversity and using these findings to build lifelike robots that exhibit truly adaptive behaviors. To this end, we have focused on one of the most primitive forms of intelligence concerning behavioral diversity, namely, a plasmodium of true slime mold. The plasmodium is a large amoeba-like unicellular organism that does not possess any nervous system or specialized organs. However, it exhibits versatile spatiotemporal oscillatory patterns and switches spontaneously between these. Inspired by the plasmodium, we built a mathematical model that exhibits versatile oscillatory patterns and spontaneously transitions between these patterns. This model demonstrates that, in contrast to coupled nonlinear oscillators with a well-designed complex diffusion network, physically interacting mechanosensory oscillators are capable of generating versatile oscillatory patterns without changing any parameters. Thus, the results are expected to shed new light on the design scheme for lifelike robots that exhibit amazingly versatile and adaptive behaviors.

This paper presents a two-dimensional simulation model of an amoeboid robot that exhibits locomotion in a decentralized manner. The significant feature to note is that the model does not control friction between parts of the robot and ground explicitly but exploits passive dynamics of the inner fluid of the robot, i.e., thixotropic flow, in order to generate locomotion. Thixotropy is a very interesting rheological property of a fluid to form a gelled structure over time when not subject to shearing and then to liquefy when agitated, which is observed in protoplasmic streaming of amoeba and plasmodium of true slime mold. Simulation results show that embedding this passive dynamics induces morphological positive feedback mechanism, leading to convection of the inner fluid, which in turn generates locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on revealing the secret of how decentralized control should be designed.

Animals exhibit astoundingly adaptive and supple locomotion under real world constraints. In order to endow robots with similar capabilities, we must implement large degrees of freedom, equivalent to animals, into the robots' bodies. For taming large degrees of freedom, the concept of autonomous decentralized control plays a pivotal role. However, a systematic way of designing such autonomous decentralized control system is still missing. Aiming at understanding the principles that underlie animals' locomotion, in our early studies, we focused on true slime mold, a primitive living organism, and extracted a decentralized control scheme. In order to validate this control scheme, this paper presents a soft-bodied amoeboid robot inspired by true slime mold. Significant features of this robot are twofold: (1) the robot has truly soft and deformable body stemming from real-time tunable springs and a balloon, the former is used for an outer skin of the body and the latter serves as protoplasm; and (2) a fully decentralized control using coupled oscillators with completely local sensory feedback mechanism is realized by exploiting the long-distance physical interaction between the body parts stemming from both the softness of the body and the law of conservation of protoplasmic mass. Experimental results show that this robot exhibits truly supple locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on design scheme for autonomous decentralized control system.

Animals exhibit astoundingly adaptive and supple locomotion under real world constraints. In order to endow robots with similar capabilities, we must implement many degrees of freedom, equivalent to animals, into the robots' bodies. For taming many degrees of freedom, the concept of autonomous decentralized control plays a pivotal role. However, a systematic way of designing such autonomous decentralized control system is still missing. Aiming at understanding the principles that underlie animals' locomotion, in our early studies, we focused on plasmodium of true slime mold, a primitive living organism, and extracted a design scheme for autonomous decentralized control system. In order to demonstrate the relevance of this design scheme, this paper presents a soft-bodied fluid-driven amoeboid robot inspired by plasmodium of true slime mold. The significant features of this robot are twofold: (1) the robot has fluidic circuit (i.e., cylinders and nylon tubes filled with fluid) and truly soft and deformable body stemming from real-time tunable springs, the former serves as protoplasm and the latter is used for elastic actuators; and (2) a fully decentralized control using coupled oscillators with completely local sensory feedback mechanism is realized by exploiting the long-distance physical interaction between the body parts stemming from the law of conservation of protoplasmic mass. The experimental results show that this robot exhibits truly supple locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on design scheme for autonomous decentralized control system.

The control and mechanical systems of an embodied agent should be tightly coupled so as to emerge useful functionalities such as adaptivity. This indicates that the mechanical system as well as the control system should be responsible for a certain amount of computation for generating the behavior. However, there still leaves much to be understood about how such “computational offloading” from the control system to the mechanical system can be achieved. In order to intensively investigate this, here we particularly focus on the “softness” of the body, and show how the computational offloading derived from this property is exploited to simplify the control system and to increase the degree of adaptivity. To this end, we employ a two-dimensional amoeboid robot as a practical example, consisting of incompressive fluid (i.e. protoplasm) covered with an outer skin composed of a network of real-time tunable springs. Preliminary simulation results show that the exploitation of the “long-distant interaction” stemming from “the law of conservation of protoplasmic mass” allows us to simplify the control mechanism; and that adaptive amoeboid locomotion can be realized without the need of a central controller. The results obtained are expected to shed light on how control and mechanical systems should be coupled, and what the “brain-body-interaction” carefully designed brings to the resulting behavior.

Traditionally, robot control has been done typically by "highly precise control algorithms": the position of each movable body part is accurately determined at any time with vast amount of computation. This, however, causes serious problems, particularly in terms of adaptability and energy efficiency. On the other hand, an extreme approach has been gaining a lot of attention recently. A good instantiation is the passive dynamic walker, driven only by exploiting the intrinsic dynamics of its mechanical system. However, the mechanical system is not everything, just as the control system is not everything; "well-balanced" coupling between control and mechanical systems should be considered. In addition, the "meeting point" between the two systems should be flexibly varied according to the environment encountered. In light of these facts, this study intensively focuses on the stiffness of robots' joints, since this effectively influences the dominance relationship between control and mechanical systems. More specifically, the aim of this study is to develop a "real-time tunable spring" that can smoothly change its elasticity without changing its natural length, allowing robot's joints to change their position and stiffness independently