This paper describes the development of a new expressive robotic head for the bipedal humanoid robot. Facial expressions of our old robotic head have low facial expression recognition rate and in order to improve it we asked amateur cartoonists to create computer graphics (CG) images. To realize such expressions found in the CGs, the new head was provided with 24-DoFs and facial color. We designed compact mechanisms that fit into the head, which dimensions are based on average adult Japanese female size. We conducted a survey with pictures and videos to evaluate the expression ability. Results showed that facial expression recognition rates for the 6 basic emotions are increased compared to the old KOBIAN's head.

Currently, an agricultural method called Synecoculture^TM has been receiving attention as a means for multiple crop production and recovering from environmental degradation; it helps in regreening the environment and establishing an augmented ecosystem with high biodiversity. In this method, several types of plants are grown densely, and their management relies mainly on manual labor, since conventional agricultural machines and robots cannot be applied in complex vegetation. To improve work efficiency and boost regreening by scaling-up Synecoculture, we developed a robot that can sow, prune, and harvest in dense and diverse vegetation that grows under solar panels, towards the achievement of compatibility between food and energy production on a large scale. We adopted a four-wheel mechanism with sufficient ability to move on uneven terrain, and a two orthogonal axes mechanism with adjusted tool positioning while performing management tasks. In the field experiment, the robot could move straight on shelving slopes and overcome obstacles, such as small steps and weeds, and succeeded in harvesting and weeding with human operation, using the tool maneuver mechanism based on the recognition of the field situation through camera image.

Many extant studies proposed various stabilizing control methods for humanoids during the stance phase while hopping and running. Although these methods contribute to stability during hopping and running, humanoid robots do not swing their legs rapidly during the flight phase to prevent rotation in the yaw direction. Humans utilize their torsos and arms when running to compensate for the angular momentum in the yaw direction generated by leg movement during the flight phase. In this study, we developed an angular momentum control method based on human motion for a humanoid upper body. The method involves calculation of the angular momentum generated by the movement of the humanoid legs and calculation of the torso and arm motions required to compensate for the angular momentum of the legs in the yaw direction. We also developed a humanoid upper-body mechanism having human link length and mass properties, using carbon-fiber-reinforced plastic and a symmetric structure for generating large angular momentum. The humanoid robot developed in this study could generate almost the same angular momentum as that of a human. Furthermore, when suspended in midair, the humanoid robot achieved angular momentum compensation in the yaw direction.

This paper describes the implementation in a walking humanoid robot of a mental model, allowing the dynamical change of the emotional state of the robot based on external stimuli; the emotional state affects the robot decisions and behavior, and it is expressed with both facial and whole-body patterns. The mental model is applied to KOBIAN-R, a 65-DoFs whole body humanoid robot designed for human-robot interaction and emotion expression. To evaluate the importance of the proposed system in the framework of human-robot interaction and communication, we conducted a survey by showing videos of the robot behaviors to a group of 30 subjects. The results show that the integration of dynamical emotion expression and locomotion makes the humanoid robot more appealing to humans, as it is perceived as more “favorable” and “useful”, and less “robot-like".

This paper describes a walking stabilization control based on gait analysis for a biped humanoid robot. We have developed a human-like foot mechanism mimicking the medial longitudinal arch to clarify the function of the foot arch structure. To evaluate the arch function through walking experiments using a robot, a walking stabilization control should also be designed based on gait analysis. Physiologists suggest the ankle, hip and stepping strategies, but these strategies are proposed by measuring human beings who are not “walking” but “standing” against force disturbances. Therefore, first we conducted gait analysis in this study, and we modeled human walking strategy enough to be implemented on humanoid robots. We obtained following two findings from gait analysis: i) a foot-landing point exists on the line joining the stance leg and the projected point of CoM on the ground, and ii) the distance between steps is modified to keep mechanical energy at the landing within a certain value. A walking stabilization control is designed based on the gait analysis. Verification of the proposed control is conducted through experiments with a human-sized humanoid robot WABIAN-2R. The experimental videos are supplemented.

Improving the energy efficiency of robots is an important issue for the widespread use of robots in society. However, previous methods plan motions to perform tasks in the shortest possible time in consideration of work efficiency. Other methods change the trajectory for same path to decrease unnecessary acceleration/deceleration. On the other hand, it would be efficient to plan a path and trajectory that proceeds to a goal position after waiting in an energy-efficient posture with low joint torque load. The energy-efficient posture is depending on a robot’s structure such as length or mass of each link, joint specifications and a spring of a joint to support weight of a robot. Furthermore, there is a possibility to improve the calculation speed by using quantum computing technology, which can solve combinatorial optimization problems at high speed. In this study, we propose a method for generating low energy-consumption motions for robots using quantum computing technology. The problem is formulated by discretizing the transitions of end-effector positions that represent the robot’s motion in terms of workspace and work time, and by using the total torque required for the motion as an objective function and constraints representing the robot’s performance and the range and time of the target work. Simulation results show that the proposed method reduces the total torque consumption by 10% compared to a simple linear motion, and the computation time could be reduced by 77%. Moreover, a torque consumption reduction of 2% was confirmed compared to the optimized motion without springs.