A Benchmarking of DCM-Based Architectures for Position, Velocity and Torque-Controlled Humanoid Robots

Abstract

This paper contributes towards the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body QP control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372 meters per second, which is the highest walking velocity achieved by the iCub robot.

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

“…A state-of-the-art control architecture for humanoid robot locomotion is composed of three nested layers that exploit both simplified and complete robot models [2].…”

“…where K ξ p > I 2 and K ξ i > 0 2 [2], ω = g/z 0 , g is the gravitational constant and z 0 denotes the constant CoM height assumed for the Linear Inverted Pendulum (LIP) model. The desired ZMP position r zmp ref is then stabilized along with the reference ground CoM position and velocity x ref , ẋref ∈ R 2 by means of the control law given by: Finally, the inner whole-body Quadratic Programming (QP) control loop computes the robot velocity ν as the solution to a stack of tasks formulation with hard and soft constraints, cast as a QP problem.…”

“…State-of-the-art architectures for humanoid locomotion simplify the whole-body trajectory generation problem by hierarchically decomposing it into several layers [1]. Layers' functionalities can be categorized [2] in: 1) Trajectory optimization, providing a high-level footstep plan given user input; 2) Simplified model control, computing feasible Center of Mass (CoM) trajectories given the footsteps; and 3) Whole-body QP control, producing dynamically-feasible joint trajectories. Instead of directly optimizing over large configuration spaces, the first two layers tend to use simplified models to compute solutions.…”

“…For instance, the unicycle planner [3] employs a unicycle model to produce footstep plans at the trajectory optimization layer, constraining the plan to simple directed walking on a plane [4], [5]. In recent years, hierarchical architectures have been successfully applied to produce robust walking on a diverse range of complex humanoids † [1], [6], [7], [8], [3], [2], [9], [10], also allowing for the integration of reactive strategies [11], [12]. However, simplified models do not fully represent the complex humanoid mechanical structure in order to reduce computational cost and allow for on-line operation.…”

Modeling of Visco-Elastic Environments for Humanoid Robot Motion Control

“…1 Authors are with Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genoa, Italy name.surname@iit.it 2 First Author is with DIBRIS, University of Genoa, Genoa, Italy programming (QP) control loop, whose main objective is to ensure the tracking of the desired trajectories by using complete robot models. In the whole-body QP control layer, the interaction between the environment and the robot is often modelled using the rigid contact assumption [6], [7], [8], [9]. Under this hypothesis, the controller can instantaneously change the contact forces to guarantee the tracking of desired quantities.…”