From walking to running: robust and 3D humanoid gait generation via MPC

Humanoid robots are platforms that can succeed in tasks conceived for humans. From locomotion in unstructured environments, to driving cars, or working in industrial plants, these robots have a potential that is yet to be disclosed in systematic every-day-life applications. Such a perspective, however, is opposed by the need of solving complex engineering problems under the hardware and software point of view. In this thesis, we focus on the software side of the problem, and in particular on locomotion control. The operativity of a legged humanoid is subordinate to its capability of realizing a reliable locomotion. In many settings, perturbations may undermine the balance and make the robot fall. Moreover, complex and dynamic motions might be required by the context, as for instance it could be needed to start running or climbing stairs to achieve a certain location in the shortest time. We present gait generation schemes based on Model Predictive Control (MPC) that tackle both the problem of robustness and tridimensional dynamic motions. The proposed control schemes adopt the typical paradigm of centroidal MPC for reference motion generation, enforcing dynamic balance through the Zero Moment Point condition, plus a whole-body controller that maps the generated trajectories to joint commands. Each of the described predictive controllers also feature a so-called stability constraint, preventing the generation of diverging Center of Mass trajectories with respect to the Zero Moment Point. Robustness is addressed by modeling the humanoid as a Linear Inverted Pendulum and devising two types of strategies. For persistent perturbations, a way to use a disturbance observer and a technique for constraint tightening (to ensure robust constraint satisfaction) are presented. In the case of impulsive pushes instead, techniques for footstep and timing adaptation are introduced. The underlying approach is to interpret robustness as a MPC feasibility problem, thus aiming at ensuring the existence of a solution for the constrained optimization problem to be solved at each iteration in spite of the perturbations. This perspective allows to devise simple solutions to complex problems, favoring a reliable real-time implementation. For the tridimensional locomotion, on the other hand, the humanoid is modeled as a Variable Height Inverted Pendulum. Based on it, a two stage MPC is introduced with particular emphasis on the implementation of the stability constraint. The overall result is a gait generation scheme that allows the robot to overcome relatively complex environments constituted by a non-flat terrain, with also the capability of realizing running gaits. The proposed methods are validated in different settings: from conceptual simulations in Matlab to validations in the DART dynamic environment, up to experimental tests on the NAO and the OP3 platforms

Feasibility-Driven Step Timing Adaptation for Robust MPC-Based Gait Generation in Humanoids

The feasibility region of a Model Predictive Control (MPC) algorithm is the subset of the state space in which the constrained optimization problem to be solved is feasible. In our recent Intrinsically Stable MPC (IS-MPC) method for humanoid gait generation, feasibility means being able to satisfy the dynamic balance condition, the kinematic constraints on footsteps as well as an explicit stability condition. Here, we exploit the feasibility concept to build a step timing adapter that, at each control cycle, modifies the duration of the current step whenever a feasibility loss is imminent due, e.g., to an external perturbation. The proposed approach allows the IS-MPC algorithm to maintain its linearity and adds a negligible computational burden to the overall scheme. Simulations and experimental results where the robot is pushed while walking showcase the performance of the proposed approach

From Walking to Running: 3D Humanoid Gait Generation via MPC

We present a real-time algorithm for humanoid 3D walking and/or running based on a Model Predictive Control (MPC) approach. The objective is to generate a stable gait that replicates as closely as possible a footstep plan, i.e., a sequence of candidate footstep positions and orientations with associated timings. For each footstep, the plan also specifies an associated reference height for the Center of Mass (CoM) and whether the robot should reach the footstep by walking or running. The scheme makes use of the Variable-Height Inverted Pendulum (VH-IP) as prediction model, generating in real-time both a CoM trajectory and adapted footsteps. The VH-IP model relates the position of the CoM to that of the Zero Moment Point (ZMP); to avoid falling, the ZMP must be inside a properly defined support region (a 3D extension of the 2D support polygon) whenever the robot is in contact with the ground. The nonlinearity of the VH-IP is handled by splitting the gait generation in two consecutive stages, both requiring to solve a quadratic program. Thanks to a particular triangular structure of the VH-IP dynamics, the first stage deals with the vertical dynamics using the Ground Reaction Force (GRF) as decision variable. Using the prediction given by the first stage, the horizontal dynamics becomes linear time-varying. During flight phases the VH-IP collapses to a free-falling mass model. The proposed formulation incorporates constraints in order to maintain physically meaningful values of the GRF, keep the ZMP in the support region during contact phases, and ensure that the adapted footsteps are kinematically realizable. Most importantly, a stability constraint is enforced on the time-varying horizontal dynamics to guarantee a bounded evolution of the CoM with respect to the ZMP. Furthermore, we show how to extend the technique in order to perform running on tilted surfaces. We also describe a simple technique that receives in input high-level velocity commands and generates a footstep plan in the form required by the proposed MPC scheme. The algorithm is validated via dynamic simulations on the full-scale humanoid robot HRP-4, as well as experiments on the small-sized robot OP3

Cow, donkey and human milk affects metabolic homeostasis and inflammatory state by modulating hepatic mitochondrial function and gut microbiota composition in rats

Cow, donkey and human milk affects metabolic homeostasis and inflammatory state by modulating hepatic mitochondrial function and gut microbiota composition in rat

Response to letter regarding article, "growth properties of cardiac stem cells are a novel biomarker of patients' outcome after coronary bypass surgery"

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Recognizing and treating myocarditis in recent-onset systemic sclerosis heart disease: Potential utility of immunosuppressive therapy in cardiac damage progression

Scleroderma heart disease is a major risk of death in systemic sclerosis (SSc). Mechanisms underlying myocardial damage are still unclear. We performed an extensive study of SSc patients with recent-onset symptoms for heart disease and examined the efficacy of immunosuppressive therapy

Growth properties of cardiac stem cells are a novel biomarker of patients' outcome after coronary bypass surgery

BACKGROUND: The efficacy of bypass surgery in patients with ischemic cardiomyopathy is not easily predictable; preoperative clinical conditions may be similar, but the outcome may differ significantly. We hypothesized that the growth reserve of cardiac stem cells (CSCs) and circulating cytokines promoting CSC activation are critical determinants of ventricular remodeling in this patient population. METHODS AND RESULTS: To document the growth kinetics of CSCs, population-doubling time, telomere length, telomerase activity, and insulin-like growth factor-1 receptor expression were measured in CSCs isolated from 38 patients undergoing bypass surgery. Additionally, the blood levels of insulin-like growth factor-1, hepatocyte growth factor, and vascular endothelial growth factor were evaluated. The variables of CSC growth were expressed as a function of the changes in wall thickness, chamber diameter and volume, ventricular mass-to-chamber volume ratio, and ejection fraction, before and 12 months after surgery. A high correlation was found between indices of CSC function and cardiac anatomy. Negative ventricular remodeling was not observed if CSCs retained a significant growth reserve. The high concentration of insulin-like growth factor-1 systemically pointed to the insulin-like growth factor-1-insulin-like growth factor-1 receptor system as a major player in the adaptive response of the myocardium. hepatocyte growth factor, a mediator of CSC migration, was also high in these patients preoperatively, as was vascular endothelial growth factor, possibly reflecting the vascular growth needed before bypass surgery. Conversely, a decline in CSC growth was coupled with wall thinning, chamber dilation, and depressed ejection fraction. CONCLUSIONS: The telomere-telomerase axis, population-doubling time, and insulin-like growth factor-1 receptor expression in CSCs, together with a high circulating level of insulin-like growth factor-1, represent a novel biomarker able to predict the evolution of ischemic cardiomyopathy following revascularization