The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this paper, a robust balance control framework for humanoids is proposed. Firstly, a Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum damping control. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated compared to a state-of-the-art Quadratic Programming-based CP controller that employs the ankle, hip, and stepping strategies.

Recently, foot stepping strategies of humanoid robots have been actively developed for robust balancing of humanoids against disturbances. In this paper, a novel stepping algorithm adjusting double support phase (DSP) time is proposed. First, the stepping algorithm is proposed based on a model predictive control (MPC) framework for capture point (CP) control and footstep adjustment. Next, when the remaining step time is not enough to adjust the footstep, the DSP scaling method brings the next swing phase forward by reducing the DSP time, which enables the robot to maintain the balance robustly. The robust balance control performance of the proposed method is validated through simulations and experiments when the robot is walking in the presence of external pushes. A more stable balancing performance is realized compared to state-of-the-art stepping controllers.

The estimation of external joint torque and contact wrench is essential for achieving stable locomotion of humanoids and safety-oriented robots. Although the contact wrench on the foot of humanoids can be measured using a force-torque sensor (FTS), FTS increases the cost, inertia, complexity, and failure possibility of the system. This paper introduces a method for learning external joint torque solely using proprioceptive sensors (encoders and IMUs) for a floating base robot. For learning, the GRU network is used and random walking data is collected. Real robot experiments demonstrate that the network can estimate the external torque and contact wrench with significantly smaller errors compared to the model-based method, momentum observer (MOB) with friction modeling. The study also validates that the estimated contact wrench can be utilized for zero moment point (ZMP) feedback control, enabling stable walking. Moreover, even when the robot's feet and the inertia of the upper body are changed, the trained network shows consistent performance with a model-based calibration. This result demonstrates the possibility of removing FTS on the robot, which reduces the disadvantages of hardware sensors.

Maintaining balance to external pushes is one of the most important features for a humanoid to walk in a real environment. In particular, methods for counteracting to pushes using the centroidal angular momentum (CAM) control have been actively developed. In this paper, a CAM control scheme based on hierarchical quadratic programming (HQP) is proposed. The scheme of the CAM control consists of CAM tracking control and initial pose return control, which is hierarchically operated based on HQP to ensure the priority of CAM tracking performance. The proposed method is implemented in a capture point (CP) feedback control framework. Through simulations and experiments, the proposed method demonstrated more stable balance control performance than the previous method when the humanoid is walking in the presence of external perturbation.

As human-robot collaboration increases and robots are applied to everyday life, interest in safety issues is increasing. To be safely used in real life, in addition to collision prevention algorithms, robots need to quickly detect unexpected collisions and take appropriate actions. Recently, deep learning-based collision detection algorithms have been proposed to overcome the limitations of model-based collision detection methods, but there are also limitations on deep learning methods, especially in data collection. The collected data are often insufficient because collecting collision data is laborious and intrinsically imbalanced, meaning that the collision data are much smaller than the free-motion data. Moreover, since collecting collision data is risky and might cause potential damage to the robot, applying the deep learning method to a new target robot on mass production is highly restricted. Therefore, in this article, an inductive bias is imposed on network structure and input variable to be sample-efficiently trained, which is suitable for insufficient imbalanced data. The proposed modularized neural network removes the connection between other joints at the front part of the network, reducing the search space of the learnable parameter. Moreover, the input variable is selected to take both dynamics features and error-related features into account. Consequently, the proposed method is versatile, showing successful generalization performance to random motion, random collision location, and various loads at the end-effector. Furthermore, to circumvent the limitations of applying deep learning methods to mass production, transfer learning is proposed, which does not require any collision data from the target robot. The proposed data-mixture method and collision ratio adjustment method for fine-tuning are validated with two source robots and one target robot. The transferred network is effective to be applied on mass production without losing performance compared to a specific-robot-trained network(a network trained with specific robot data and applied to the same robot).

As robots begin to collaborate with people in real life, safety needs to be rigorously ensured to reliably employ robots nearby. In addition to collision prevention algorithms, studies are being actively conducted on collision handling methods. Momentum Observer (MOB) was developed to estimate disturbance torque without using joint acceleration. However, the estimated disturbance from MOB contains not only the applied external torque but also model uncertainty such as friction and modeling error due to imprecise system identification. Our proposed method handles this problem by learning the model uncertainty with Long Short-Term Memory (LSTM) and thereby estimates the purely applied external torque with only proprioceptive sensors. The proposed method can be applied even when the information on the robot model is not available. The experiments using a real robot show that the external torque can be estimated and collisions can be detected accordingly even in a limited situation where a precise dynamics model and friction model are not available.

The compliant motion of humanoid robots is one of their most important characteristics for interacting with humans and various environments in the real world. During walking, compliant motion ensures stable contact between the foot and ground, but walking stability is degraded by position tracking performance and unknown disturbances. To address the issue of instability of humanoid robot walking with compliant motion control, this paper proposes a model for real-time walking pattern generation considering the motion control performance of a robot. The dynamic model of a robot with a motion controller is described as a second-order system approximating position tracking performance with a linear inverted pendulum model to determine the relationship between the zero-moment point and center of mass (CoM). The CoM trajectory is calculated using preview control based on the dynamics model and current state of the robot. Therefore, even if the robot has the low tracking performance due to compliant motion control, the walking stability can be ensured. The proposed method was implemented on our humanoid robot, DYROS-JET, and its performance was demonstrated through improved stability during walking.