Online Centroidal Angular Momentum Reference Generation and Motion Optimization for Humanoid Push Recovery

This letter presents a new push recovery algorithm for humanoid robots in balancing scenarios by exploiting the system's rotational dynamics. The proposed framework actively generates centroidal angular momentum (CAM) references based on the force magnitude and direction of the push to counteract the disturbance and maintain its balance. Since a humanoid robot can only store a limited amount of angular momentum, the CAM reference is generated in three consecutive phases: 1) CAM generation phase to counteract the push; 2) CAM reduction phase to bring the robot to a halt; and 3) posture recovery phase to converge to the reference pose again. A subsequent whole-body motion optimizer, formulated as a constrained quadratic optimization problem, generates kinematically feasible whole-body trajectories based on the CAM reference. The proposed framework is validated through experiments with the humanoid robot TORO

Online Learning of Centroidal Angular Momentum Towards Enhancing DCM-Based Locomotion

Gait generation frameworks for humanoid robots typically assume a constant centroidal angular momentum (CAM) throughout the walking cycle, which induces undesirable contact torques in the feet and results in performance degradation. In this work, we present a novel algorithm to learn the CAM online and include the obtained knowledge within the closed-form solutions of the Divergent Component of Motion (DCM) locomotion framework. To ensure a reduction of the contact torques at the desired center of pressure position, a CAM trajectory is generated and explicitly tracked by a whole-body controller. Experiments with the humanoid robot TORO demonstrate that the proposed method substantially increases the maximum step length and walking speed during locomotion

Unified Motion Planner for Walking, Running, and Jumping Using the Three-Dimensional Divergent Component of Motion

Running and jumping are locomotion modes that allow legged robots to rapidly traverse great distances and overcome difficult terrain. In this article, we show that the 3-D divergent component of motion (3D-DCM) framework, which was successfully used for generating walking trajectories in previous works, retains its validity and coherence during flight phases, and, therefore, can be used for planning running and jumping motions. We propose a highly efficient motion planner that generates stable center-of-mass (CoM) trajectories for running and jumping with arbitrary contact sequences and time parametrizations. The proposed planner constructs the complete motion plan as a sequence of motion phases that can be of different types: stance, flight, transition phases, etc. We introduce a unified formulation of the CoM and DCM waypoints at the start and end of each motion phase, which makes the framework extensible and enables the efficient waypoint computation in matrix and algorithmic form. The feasibility of the generated reference trajectories is demonstrated by extensive whole-body simulations with the humanoid robot TORO

From Space to Earth - Relative-CoM-to-Foot (RCF) control yields high contact robustness

This paper introduces the Simplest Articulated Free-Floating (SAFF) model, a low-dimensional model facilitating the examination of controllers, which are designed for freefloating robots that are subject to gravity. Two different stateof-the-art control approaches, namely absolute CoM control accompanied by an assumption about the foot acceleration, and a controller combining absolute CoM and foot control objectives, are shown to yield exponential stability in the nominal case, while becoming unstable if the foot contact is lost. As an improvement over the state of the art, the so-called Relative-CoM-to-Foot (RCF) controller is introduced, which again yields exponential stability nominally, while preserving a BIBO stable behavior even in case of a complete contact loss. The controller performance is validated in various simulations

MPTC - Modular Passive Tracking Controller for stack of tasks based control frameworks

This work introduces the so-called Modular Passive Tracking Controller (MPTC), a generic passivity-based controller, which aims at independently fulfilling several subtask objectives. These are combined in a stack of tasks (SoT) that serves as a basis for the synthesis of an overall system controller. The corresponding analysis and controller design are based on Lyapunov theory. An important contribution of this work is the design of a specific optimization weighting matrix that ensures passivity of an overdetermined and thus conflicting task setup. The proposed framework is validated through simulations and experiments for both fixed-base and free-floating robots

Agile and Dynamic Standing-up Control for Humanoids using 3D Divergent Component of Motion in Multi-contact Scenario

Standing-up is a task that humanoids need to be able to perform in order to be employed in real-world scenarios. This letter proposes a new robust strategy for a humanoid to stand up in challenging scenarios where no completely preplanned motion can accomplish the same task. This strategy exploits the concept of three-dimensional divergent component of motion and passivity-based whole-body control. The latter firstly maximizes the push forces applied to the robot's center of mass to make agile whole-body recovery motion. Then, during the rising phase, it reduces these forces to zero and stabilizes the robot in an upward position. Optimization of centroidal angular momentum is fully integrated into the proposed whole-body standing-up control to create the trajectories of the hip and the upper body joints online. The effectiveness of the proposed method is validated in simulations and experiments on the humanoid TORO