Masahiro Shimizu

Cerebral organoid, that is cultured from iPS cells, is known as a counterpart of the human brain. Therefore, we expect that cerebral organoids can be used as processing units made of biological materials for bio-machine hybrid systems. Related works have demonstrated the potential of cerebral organoids as processing units. However, no application of cerebral organoids as learning intelligent systems has been demonstrated. We have previously developed a prototype robot in which cerebral organoids were co-cultured with muscle cells. As a next step, it is necessary to clarify how cerebral organoids can be used as living CPUs for novel intelligent systems in robotics as well. In this study, we consider how to easily use cerebral organoids as the CPU of a robot. Therefore, we consider a measurement system that can measure the local field potential of cerebral organoids while simultaneously observing them with an optical microscope. Furthermore, we consider not only measurement but also applying electrical stimuli to the cerebral organoids from the outside. The measurement system should be designed for easy mounting of cerebral organoids onto robotic systems. The shape of the measurement chamber and electrodes should be simple enough to allow seamless connection to traditional instruments and control systems. These design aspects enhance the system's usability. Based on the above discussion, the purpose of this study is to develop a cartridge-type measurement system for cerebral organoids that will facilitate their application as intelligent systems. This study developed a cartridge-type measurement system for cerebral organoids that will facilitate their application as intelligent systems. This paper described the specifications of the cartridge-type measurement system developed. We explained the method for culturing cerebral organoids. We conducted measuring the local field potential of cerebral organoids using the proposed cartridge-type measurement system. We demonstrated the successful measurement of local field potentials in multiple individual cerebral organoids. We discussed the perspective of learning by cerebral organoids and possible applications of the proposed system. In the future, we expect that our research will lead to novel regenerative medicine with cyborg technology.

Recent advances in bionics have made it possible to create various tissue and organs. Using this cell culture technology, engineers have developed a robot driven by three-dimensional cultured muscle cells (bioactuator)—a muscle cell robot. For more applications, researchers have been developed various tissues and organs with bio3D printer. However, three-dimensional cultured muscle cells printed by bio3D printer have been not used for muscle cell robot yet. The aim of our study is to develop easy fabrication method of bioactuator having high design flexibility like as bio3D printer. We fabricated three-dimensional cultured muscle cells using mold and dish having pin which can contribute to shape and cell alignment. In this study, we observed that our method maintained the shape of three-dimensional cultured muscle cells and caused cell alignment which is important for bioactuator development. We named three-dimensional cultured muscle cells developed in this study “bio-cultured artificial muscle (BiCAM)”. Finally, we observed that BiCAM contracted in response to electrical stimulus. From these data, we concluded our proposed method is easy fabrication method of bioactuator having high design flexibility.

The past ten years have seen the rapid expansion of the field of biohybrid robotics. By combining engineered, synthetic components with living biological materials, new robotics solutions have been developed that harness the adaptability of living muscles, the sensitivity of living sensory cells, and even the computational abilities of living neurons. Biohybrid robotics has taken the popular and scientific media by storm with advances in the field, moving biohybrid robotics out of science fiction and into real science and engineering. So how did we get here, and where should the field of biohybrid robotics go next? In this perspective, we first provide the historical context of crucial subareas of biohybrid robotics by reviewing the past 10+ years of advances in microorganism-bots and sperm-bots, cyborgs, and tissue-based robots. We then present critical challenges facing the field and provide our perspectives on the vital future steps toward creating autonomous living machines.

Storing elastic energy in a soft body and releasing it instantly enables ultrafast movement beyond muscle capability, which small animals (mainly arthropods) realize. We applied this mechanism to a soft robot to achieve locomotion on top of a tubular surface such as a branch (i.e., upside-down brachiation). To achieve arboreal locomotion, the robot must have strong grippers to support its body and minimize the duration of time when one gripper is off the branch. By using a simulation model and prototype, we demonstrated that storing and releasing elastic energy in a soft body satisfies these requirements, allowing us to fabricate a lightweight robot. The prototype completed one step of the locomotion process in 0.22 secs, making it 2.3 times faster than the original speed of the mounted motor. In addition, we confirmed that the robot climbs 0- to 90-degree branches.

Soft robotics can dramatically increase the affinity between machines and biological systems. Designing the machine/device to be soft and deformable allows the biological system to interact with the robotic system(s) mechanically, electronically, and chemically. This advantage is evident from the rapid growth of collaborative robotics, where a robot can be mechanically guided by an operator to learn motions from them without the need for coding. This letter introduces a method for combining a soft robotic system with a biological system, demonstrated through a series of case studies of ongoing research projects. These various projects have a common purpose in creating self-modifying bio-soft robots.