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This paper focuses on a transition motion between bipedal walking and running, whose characteristics have been revealed through numerous biological experiments. Although hysteresis in walk-to-run and run-to-walk transitions, the amount of which is proportional to the magnitude of acceleration/deceleration, is observed, it has not been elucidated yet. This is due to difficulty to conduct any biological experiments without the hysteresis in locomotion. From the viewpoint of nonlinear dynamics, the hysteresis is caused by dependency of the state history. Delays in perception and action for the walk–run transition can be assumed as the cause of the hysteresis. This paper therefore investigates i) whether the delays really cause the hysteresis in the walk–run transition, and ii) how they contribute to locomotion performance. To this end, in numerical simulations, we employ a controller for bipedal gait, named unified bipedal gait (UBG), which has capabilities of walking, running, and transitions between them like bipeds do. UBG is first optimized in pursue of biologically plausible controller with the same characteristics about the transition motion for reliable investigation. This optimized UBG reveals that the delays in perception and action actually cause the hysteresis with linearity of its amount and the magnitude of acceleration/deceleration. In addition, the delays play the key role of a bridge between latent representations of walking and running, thereby improving the success rate of the transition by nearly 40% from the case without the delays.

In this paper, we propose an adaptive speed controller (ASC) for a limit-cycle-based gait classified as a three-dimensional (3-D) bipedal gait. The limit-cycle-based gait of bipedal robots is excellent for energy efficiency. However, the forward and sideward moving speeds of these robots are difficult to control since they are autonomous systems and their behavior depends upon the initial states of every step. Therefore, in this study, we design a swing leg motion to appropriately adjust the initial states of every step. First, the forward speed is controlled by injecting the momentum of the swing leg, which shifts an energy equilibrium point from the original point to the reference point where the reference forward speed is achieved. Second, the sideward speed is controlled by asymmetric touchdown positions on the right and the left legs, which separate the limit cycle into two different limit cycles for each leg, and this separation leads to the sideward motion. The proposed ASC can be easily applied to an arbitrary limit-cycle-based gait because it is independent of the dynamics of the gait model. The performance of the proposed ASC was validated in terms of the residual error, step response, and disturbance rejection capabilities in numerical simulations for two examples of walking. The proposed ASC also achieved a highly energy-efficient gait, which was consistent with the analytical results. Further, the proposed ASC could adapt a robot to various environments that contained up and down slopes or an obstacle.

A major challenge in the development of evolvable, autonomous, and programmable biomolecular machines is the introduction of the ability to cope with external changes. In the present study, DNA strand displacement was chosen as the main framework for modeling a DNA circuit capable of complex computational mechanisms such as decision making and reinforcement learning. Our goal was to design a DNA-based system that is adaptive and reactive to external stimuli. Our design is based on a computational algorithm inspired by a natural system, namely the ant foraging system, additionally consisting of geometrical components such as nanostructures or DNA origami. The correctness of our algorithm was verified in silico via quantitative measurement of reaction kinetics. Our results indicate that the circuit design responds correspondingly, regardless of the initial conditions under limited threshold (in contrast to the currently available DNA strand displacement systems). Furthermore, we discuss potential applications, which include decision making capable machine, and reusable DNA circuits.

This paper addresses a reference gait speed generator for limit-cycle-based bipedal gait of humanoid robots in order to achieve secure and efficient acceleration and deceleration without falling down at respective gait speeds. The reference gait speed generator is hard to be developed by analytical approaches due to the necessity of barren work for identifying the respective basins of attraction. We challenge this issue by designing virtual dynamics among a robot, a virtual leader point, and a goal, and adapting it according to a falling risk of the robot. The virtual dynamics, which has settling and acceleration times as design parameters, gives the reference speeds derived from states of the robot and the leader point to a gait speeds controller. In the dynamics, the robot’s mass is optimized virtually to maximize efficiency while ensuring stability stochastically by using a selection algorithm for locomotion. Even when there were obstacles or an up slope in traveling courses of simulations, the robot achieved the autonomous traveling from the start to the goal securely. Specific resistance was also kept small in comparison with local-stability-based walking. The proposed method makes the limit-cycle-based bipedal gait more practical and contributes toward replacing the major method that ensures stability of every step.

A visual assistance system has become attractive as a technique to improve the efficiency and stability of remote control. While an operator controls a working robot, another autonomous monitoring robot evaluates a suitable viewpoint to observe the work site, and dynamically moves to the optimal viewpoint for monitoring. Choosing the observation region (ROI: region of interest) is equivalent to deciding the action of the following autonomous monitoring system. Therefore, we focus on ROI detection in our visual support system. We propose an ROI selection method to identify the most suitable observation point and interobject relations. The monitoring robot detects a gestalt of the scene in order to identify the relations between objects. Such an adaptive ROI in real time improves the efficiency of the remote control. The experimental results indicate the effectiveness of the proposed system in terms of execution time and number of errors.

Here, we report the multicomponent assembly and disassembly processes involving DNA strand displacement to construct molecular robots. The framework for development of molecular robots designates the components as parts of the robot. Molecular recognition is used to control the reversible processes of assembly and disassembly of multiple components. The molecular recognition system identifies not only a single-strand DNA but also a microribonucleic acid as molecular stimuli to control the processes. The processes were demonstrated by gel electrophoresis, fluorescence assays, and atomic force microscopy.

The fast and effective generation of a concentration gradient by mixing in the microchannel is important for many microfluidic applications. The active control of gradient is useful for applying the measurement of cell responses by dynamic change of environment. The main purpose of this paper is the generation of temporally stable concentration gradient actively. For this purpose, the wave-like pattern of the liquid-liquid interface is produced in the microchannel. In this technique, the high frequency of the wave-like pattern is necessary for reducing the length of the mixing path. High frequency of the wave-like pattern is achieved by employing the newly developed microvalve using tailor-made multilayer piezoelectric actuators (TAMPA) that is compact yet produces large displacements and forces. This paper first details the concept for the concentration gradient generation method. Next, a microvalve (20 x 15 x 15 mm) was designed and produced using TAMPA (8.5 x 10 x 10 mm). Finally, a concentration gradient in two-layered flow was generated with the microvalve. As a result, the generation of a concentration gradient in two-layered flow with active mixing was achieved. Furthermore, it is shown that the concentration gradient can be controlled actively by adjusting the input voltage to TAMPA.

Background: Visual recognition in multi-robot systems is afflicted with a peculiar problem that observations made from different viewpoints present different perspectives. Hence, realizing cognitive sharing of the object among robots in an unconstructed environment has become challenging. To cope with these issues, we have proposed the Hierarchical Invariants Perception Model (HIPM) in which multiple representations of the target are dynamically evaluated and selected by the robot. In this paper, we propose consensus-making algorithms to acquire a viewpoint-invariant representation of the geometric relation, which is an unaddressed issue in the HIPM. Methods: The target is described by a combination of three representations: color, shape, and geometric relation. In terms of geometric relation, we employ relative positions between the target and the salient objects, which we call a geometric-relation-based representation (GRR). A GRR is regarded as viewpoint-invariant when satisfying two conditions: (i) It consists of sharable objects and (ii) the number of target candidates, which is reduced by using the GRR, is equivalent among the robots. Based on this definition, consensus-making algorithms are formulated Results: Experiments with real-world robots demonstrated that robots perceived the viewpoint-invariant GRR even when objects are occluded or their appearance changes. The experiment also demonstrated that the proposed algorithms were able to reduce the candidates without succumbing to an infinite loop. The success rate of cognitive sharing was about 60%. However, the success rate was 100% when GRR was shared. As long as robots can share the GRR, cognitive sharing may be realized even if the environment is more unstructured and uncertainties increase.