In this paper, we introduce a novel transformable aerial robot called DRAGON, which is a dual-rotor embedded multilink robot with the ability of multi-degree-of-freedom (DoF) aerial transformation. The new aerial robot can control the full pose in SE(3) regarding the center of gravity (CoG) of multilinks and can render the multi-DoF aerial transformation, which is accomplished by the original 2DoF force vectoring mechanism called dual-rotor gimbal module. The dynamics is derived on the basis of the special definition of CoG orientation, followed by a control method decoupled into thrust force control and rotor gimbal control. The minimum force norm is considered in the thrust force control to avoid force saturation, and the rotor gimbal control method is developed to enhance both translational and rotational stabilities during hovering and large-scale aerial transformation. The prototype composed from four links is constructed, and associated preliminary experiments are performed. The feasibility of the novel mechanical design and the proposed control method for the aerial transformation is demonstrated.

In this article, we describe a system design approach for the teleoperation of a whole-body bilateral humanoid robot. The conventional approach for the teleoperation of a unilateral/bilateral humanoid robot has several problems related to bipedal locomotion. In this work, we have focused on the bipedal stability and 2D/3D locomotion space problems. Our proposed system is primarily composed of bipedal-oriented assistive subsystems and a seat-type whole-body exoskeleton cockpit called "TABLIS". The bipedal-oriented assistive subsystems prevent the humanoid from falling down in cases where an operator provides an inaccurate input. TABLIS reproduces a remote 2D/3D ground surface and overcomes the space limitation of locomotion, such as the use of treadmills. We have achieved bilateral quasi-3D step traversing using our system in a demonstration experiment.

This paper describes integrated/intelligent humanoid robot system for daily-life environment tasks. We have realized complex behaviors of a humanoid robot in daily-life environment based on motion planner technique using an environment and manipulation knowledge. However in order to adapt to unknown or dynamic situations, sensor based behavior variation is essentially important. In this paper, we present a design and implementation of sensor based behavior verification system using an environment and manipulation knowledge, which is also used in manipulation motion planner. We also present software architecture that allows us to write a single stream code to perform complex concurrent humanoid motions. By using this architecture, sensor based verification functions are easily integrated in motion generation functions. Finally, we demonstrated a water-pouring task and a dishwashing task of the life-sized humanoid robot HRP2-JSK in a real environment while verifying its own motion

The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle lengths from the actual robot is necessary. In this study, we propose a system which runs the learning control mechanism for a long time to keep the self-body image of the musculoskeletal humanoid correct at all times. Also, we show that the musculoskeletal humanoid can conduct position control, torque control, and variable stiffness control using this self-body image. We conduct a long-time self-body image acquisition experiment lasting 3 hours, evaluate variable stiffness control using the self-body image, etc., and discuss the superiority and practicality of the self-body image acquisition of musculoskeletal structures, comprehensively.

The body structures of tendon-driven musculoskeletal humanoids are complex, and accurate modeling is difficult, because they are made by imitating the body structures of human beings. For this reason, we have not been able to move them accurately like ordinary humanoids driven by actuators in each axis, and large internal muscle tension and slack of tendon wires have emerged by the model error between its geometric model and the actual robot. Therefore, we construct a joint-muscle mapping (JMM) using a neural network (NN), which expresses a nonlinear relationship between joint angles and muscle lengths, and aim to move tendon-driven musculoskeletal humanoids accurately by updating the JMM online from data of the actual robot. In this study, the JMM is updated online by using the vision of the robot so that it moves to the correct position (Vision Updater). Also, we execute another update to modify muscle antagonisms correctly (Antagonism Updater). By using these two updaters, the error between the target and actual joint angles decrease to about 40% in 5 minutes, and we show through a manipulation experiment that the tendon-driven musculoskeletal humanoid Kengoro becomes able to move as intended. This novel system can adapt to the state change and growth of robots, because it updates the JMM online successively.

The body structure of an anatomically correct tendon-driven musculoskeletal humanoid is complex, and the difference between its geometric model and the actual robot is very large because expressing the complex routes of tendon wires in a geometric model is very difficult. If we move a tendon-driven musculoskeletal humanoid by the tendon wire lengths of the geometric model, unintended muscle tension and slack will emerge. In some cases, this can lead to the wreckage of the actual robot. To solve this problem, we focused on reciprocal innervation in the human nervous system, and then implemented antagonist inhibition control (AIC) based on the reflex. This control makes it possible to avoid unnecessary internal muscle tension and slack of tendon wires caused by model error, and to perform wide range motion safely for a long time. To verify its effectiveness, we applied AIC to the upper limb of the tendon-driven musculoskeletal humanoid, Kengoro, and succeeded in dangling for 14 minutes and doing pull-ups.