For trajectory generation of whole-body jumping motions such as humanoid backflips, it is crucial to simultaneously optimize the takeoff, flight, and landing phases while considering full-body dynamics and kinematics. Although such methods have been proposed for standard jumping motions, they have not been applied to more dynamic actions such as frontflips, backflips, and yaw twist jumps, where strong nonlinearity and high sensitivity to certain parameters (e.g., rotor inertia and torque cost weights) pose significant challenges. To address these challenges, we apply a two-stage optimization strategy to an existing full-body dynamics optimization method that simultaneously optimizes the takeoff, flight, and landing phases. In our approach, the same initialization and reference trajectory generation rules are shared across motions, and the solution from the first optimization is used not only as an initial guess but also as a reference in the second optimization. This strategy improves the convergence of the KKT residuals across various jump types and mitigates sensitivity to parameters such as rotor inertia and torque cost weights. As a result, our method achieves unified trajectory generation for frontflips, backflips, yaw twist jumps, and standard jumps using the same initialization, cost weights, and constraints. We also analyze the sensitivity to rotor inertia and show that exceeding a certain threshold can lead to a sharp deterioration in KKT residual convergence.

Various methods have been proposed to achieve high output torque and a wide output range for fast and high-load robotic motions. However, in robots composed of slender frames, such as humanoid robots, the limited space available for actuators and transmission components restricts the application of conventional methods. In this paper, we propose a Variable Chain Motor (VC Motor), an electric actuator that features both shape variability and speed-torque characteristics variability. Shape variability refers to the ability of the actuator to change its form during operation. This property enhances output torque by enabling a dense motor arrangement even under spatial constraints imposed by the frame structure. For example, the actuator can be placed across adjacent frames and deform according to joint rotation. Speed-torque characteristics variability allows switching output characteristics during operation using a dedicated electrical circuit. This enables an expanded range of output speed and torque without significantly increasing size or weight. We evaluated the performance of the developed VC Motor by measuring output torque and efficiency. Furthermore, by applying the VC Motor to the elbow joint of a humanoid robot, we demonstrated its capability for high-speed and high-load operations.

Transferring human motion skills to humanoid robots remains a significant challenge. In this study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid robots to replicate natural whole-body locomotion patterns and execute seamless transitions by mimicking human motions. First, we present a unified primitive-skeleton motion retargeting to mitigate morphological differences between arbitrary human demonstrators and humanoid robots. An adversarial critic component is integrated with Reinforcement Learning (RL) to guide the control policy to produce behaviors aligned with the data distribution of mixed reference motions. Additionally, we employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1 distance with a novel soft boundary constraint to stabilize the training process and prevent model collapse. Our system is evaluated on a full-sized humanoid JAXON in the simulator. The resulting control policy demonstrates a wide range of locomotion patterns, including standing, push-recovery, squat walking, humanlike straight-leg walking, and dynamic running. Notably, even in the absence of transition motions in the demonstration dataset, the robot showcases an emerging ability to transit naturally between distinct locomotion patterns as desired speed changes.

For humanoid whole body contact motions, it is important to recognize the existence of whole body contacts and the contact forces. The challenges in recognizing the existence of whole body contacts and the contact forces in life-size humanoids are: 1) the measurement part with low mechanical strength must be tolerant of high load and 2) it is difficult to model thick elastic bodies with high impact tolerance and uneven sensor placements when applied to various shapes of the whole body. This paper proposes a method of constructing a load tolerant tactile sensor by separating the loaded part from the measuring part with magnetism and protecting the measuring part inside the frame of the robot. For modeling difficulties, this paper proposes learning the relationship between the change in the detected physical quantity due to deformation of the elastic body and the contact force distribution. This paper shows through experiments that the proposed tactile sensor based on a robot frame is load tolerant enough to support the weight of a life-sized humanoid, and that it can acquire contact force distribution and the robot is able to acclimate to external forces.

Humanoids operate in repeated contact and non-contact with their environment and so the motion of humanoids such as walking on uneven terrain or in a narrow space requires the accurate force and position control. Joint torque control systems are suitable for position and force control, but are prone to friction and other modeling errors. To solve this problem, methods have been proposed to realize torque control in combination with joint position control systems or by improving joint structures such as sensors and actuators, but these methods have problems such as response delay and increased weight and volume. Thus, it is difficult to achieve motion of life-sized humanoids by whole-body torque control. In this paper, we solve challenges not with one specific layer, but rather with multiple layers that complement each other. We propose a hierarchical whole-body torque control method using four layers: friction compensation based on a vibration-suppressed model, whole-body resolved acceleration control using priority, center-of-gravity acceleration control based on foot-guided control, and landing position time modification based on capture point. We verify through walking experiments that the proposed methods can control the life-sized humanoid robot driven by high-reduction ratio joints by whole-body torque control without a torque sensor or joint position control, and that it enables the robot to move and even transport an object on outdoor uneven terrain.