In this paper, as an approach to stabilize humanoid walking where the height of CoM varies, a Novel Model Predictive Control framework based on three dimensional Divergent Component of Motion (3D-DCM) is proposed. To ensure the feasible utilization of contact forces for maintaining humanoid balance, constraints on the control inputs, Virtual Repellent Point (VRP) and footstep adjustment, and their correlation are analytically formulated into a quadratic form, resulting a Quadratically Constrained Quadratic Programming. Additionally, to enable the humanoid robot to withstand disturbances over a broader range of strides or safely navigates various terrains without encountering knee stretch, the distance between the CoM and the foot is constrained in the 3D-CoM trajectory planner. The effectiveness of the proposed method is validated through simulations and real-robot experiments in scenarios involving external disturbances and step down.

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.