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.

In this paper, a learning-based whole-body loco-motion controller is proposed, which enables quadruped robots to perform running in the style of real animals. We use a low-level controller based on multi-rigid body dynamics to calculate desired torques for each joint, while the high-level neural network policy planning the expected gait and foothold. The policy is trained with reinforcement learning, so that the robot can track a variety of trajectories according to the gait patterns recorded from real-world dogs. We transfer the walking and running gait style to quadrupeds in simulation, involving pace, trot, high-speed gallop and natural transitions. The performance is evaluated by the synchronization rate of contact state between the policy result and the recorded sequence. In the experiments, the robot runs steadily at a speed of 2 m/s and showcases a notable synchronization rate of about 80%. Without prior knowledge, the policy demonstrates a realistic foothold distribution that covers the central area of the torso, which is prevalent in running animals.