This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dynamics in the task space satisfying contact constraints. This approach is known as operational-space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imitation where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern generator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment.

This book is for researchers, engineers, and students who are willing to understand how humanoid robots move and be controlled. The book starts with an overview of the humanoid robotics research history and state of the art. Then it explains the required mathematics and physics such as kinematics of multi-body system, Zero-Moment Point (ZMP) and its relationship with body motion. Biped walking control is discussed in depth, since it is one of the main interests of humanoid robotics. Various topics of the whole body motion generation are also discussed. Finally multi-body dynamics is presented to simulate the complete dynamic behavior of a humanoid robot. Throughout the book, Matlab codes are shown to test the algorithms and to help the reader ́s understanding.

Humanoid Robots: Modeling and Control provides systematic presentation of the models used in the analysis, design and control of humanoid robots. The book starts with a historical overview of the field, a summary of the current state of the art achievements and an outline of the related fields of research. It moves on to explain the theoretical foundations in terms of kinematic, kineto-static and dynamic relations. Further on, a detailed overview of biped balance control approaches is presented. Models and control algorithms for cooperative object manipulation with a multi-finger hand, a dual-arm and a multi-robot system are also discussed. One of the chapters is devoted to selected topics from the area of motion generation and control and their applications. The final chapter focuses on simulation environments, specifically on the step-by-step design of a simulator using the Matlab® environment and tools. This book will benefit readers with an advanced level of understanding of robotics, mechanics and control such as graduate students, academic and industrial researchers and professional engineers. Researchers in the related fields of multi-legged robots, biomechanics, physical therapy and physics-based computer animation of articulated figures can also benefit from the models and computational algorithms presented in the book. Provides a firm theoretical basis for modelling and control algorithm design Gives a systematic presentation of models and control algorithms Contains numerous implementation examples demonstrated with 43 video clips

This book focuses on two issues related to human figures: realtime dynamics computation and interactive motion generation. In spite of the growing interest in human figures as both physical robots and virtual characters, standard algorithms and tools for their kinematics and dynamics computation have not been investigated very much. "Simulating and Generating Motions of Human Figures" presents original algorithms to simulate, analyze, generate and control motions of human figures, all focusing on realtime and interactive computation. The book provides both practical methods for contact/collision simulation essential for the simulation of humanoid robots and virtual characters and a general framework for online, interactive motion generation of human figures based on the dynamics simulation algorithms.

This book aims at providing algorithms for balance control of legged, torque-controlled humanoid robots. A humanoid robot normally uses the feet for locomotion. This paradigm is extended by addressing the challenge of multi-contact balancing, which allows a humanoid robot to exploit an arbitrary number of contacts for support. Using multiple contacts increases the size of the support polygon, which in turn leads to an increased robustness of the stance and to an increased kinematic workspace of the robot. Both are important features for facilitating a transition of humanoid robots from research laboratories to real-world applications, where they are confronted with multiple challenging scenarios, such as climbing stairs and ladders, traversing debris, handling heavy loads, or working in confined spaces. The distribution of forces and torques among the multiple contacts is a challenging aspect of the problem, which arises from the closed kinematic chain given by the robot and its environment.

This thesis studies the question of whole body motion generation for anthropomorphic systems. Within this work, the problem of modeling and control is considered by addressing the difficult issue of generating human-like motion. First, a dynamic model of the humanoid robot HRP-2 is elaborated based on the recursive Newton-Euler algorithm for spatial vectors. A new dynamic control scheme is then developed adopting a cascade of quadratic programs (QP) optimizing the cost functions and computing the torque control while satisfying equality and inequality constraints. The cascade of the quadratic programs is defined by a stack of tasks associated to a priority order. Next, we propose a unified formulation of the planar contact constraints, and we demonstrate that the proposed method allows taking into account multiple non coplanar contacts and generalizes the common ZMP constraint when only the feet are in contact with the ground. Then, we link the algorithms of motion generation resulting from robotics to the human motion capture tools by developing an original method of motion generation aiming at the imitation of the human motion. This method is based on the reshaping of the captured data and the motion editing by using the hierarchical solver previously introduced and the definition of dynamic tasks and constraints. This original method allows adjusting a captured human motion in order to reliably reproduce it on a humanoid while respecting its own dynamics. Finally, in order to simulate movements resembling to those of humans, we develop an anthropomorphic model with higher number of degrees of freedom than the one of HRP-2. The generic solver is used to simulate motion on this new model. A sequence of tasks is defined to describe a scenario played by a human. By a simple qualitative analysis of motion, we demonstrate that taking into account the dynamics provides a natural way to generate human-like movements.

Anthropomorphic locomotion is a complex process that involves a very large number of degrees of freedom, the human body having more than three hundred joints against thirty in humanoid robots. Taken as a whole, these degrees of freedom show a certain coherence making it possible to set the anthropomorphic system in motion and maintain its equilibrium, in order to avoid falling. This thesis highlights the computational foundations behind this orchestration. It introduces a unified mathematical framework allowing both the study of human locomotion and the generation of locomotive trajectories for humanoid robots. This framework consists of a reduction of the body-complete dynamics of the system to consider only its projection around the center of gravity, also called centroid dynamics. Although reduced, we show that this centroidal dynamics plays a central role in the understanding and formation of locomotive movements. To do this, we first establish the observability conditions of this dynamic, that is to say that we show to what extent this data can be apprehended from sensors commonly used in biomechanics and robotics. Based on these observability conditions, we propose an estimator able to reconstruct the unbiased position of the center of gravity. From this estimator and the acquisition of walking motions on various subjects, we highlight the presence of a cycloidal pattern of the center of gravity in the sagittal plane when the human is walking nominally, that is, to say without thinking. The presence of this motif suggests the existence of a motor synergy hitherto unknown, supporting the theory of a general coordination of movements during locomotion. The last contribution of this thesis is on multi-contact locomotion. Humans have remarkable agility to perform locomotive movements that require joint use of the arms and legs, such as when climbing a rock wall. How to equip humanoid robots with such capabilities? The difficulty is certainly not technological, since current robots are able to develop sufficient mechanical powers. Their performances, evaluated both in terms of quality of movement and computing time, remain very limited. In this thesis, we address the problem of generating multi-contact trajectories in the form of an optimal control problem. The interest of this formulation is to start from the reduced model of centroid dynamics while responding to equilibrium constraints. The original idea is to maximize the likelihood of this reduced dynamic with respect to body-complete dynamics. It is based on learning a measurement of occupation that reflects the kinematic and dynamic capabilities of the robot. It is effective: the resulting algorithmic is compatible with real-time applications. The approach has been successfully evaluated on the humanoid robot HRP-2, on several modes of locomotion, thus demonstrating its versatility.

The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.

Robustly maintaining balance on two legs is an important challenge for humanoid robots. The work presented in this book represents a contribution to this area. It investigates efficient methods for the decision-making from internal sensors about whether and where to step, several improvements to efficient whole-body postural balancing methods, and proposes and evaluates a novel method for efficient recovery step generation, leveraging human examples and simulation-based reinforcement learning.

The fundamental objective of robotics is to enhance the productivity of humans while interacting in potentially unstructured environments. In this sense, Human-centered robots must be fast, stable, and robust when performing varied and complicated tasks during mission execution. Although industrial robots have seen some advancements regarding motion planning and control, they are largely limited to simple pre-defined tasks in structured environments. However, to achieve highly dynamic motions for dexterous manipulation or agile locomotion in complex robots, we need to consider the use of nonlinear dynamics, complex constraints, multiple contacts, disturbances, and uncertainties. These are fundamental requirements needed to advance the use of general purpose robots dynamically interacting in a wider variety of environments. Therefore, this thesis addresses challenges that arise from the employment of optimization techniques and sophisticated realtime algorithms for the control and deployment of realistic and practical robots in human environments. Considering the above challenges, we propose efficient trajectory generation and trajectory tracking methods as the next paradigms for whole-body control (WBC). First, we formulate a class of motion planning problems to directly obtain dynamically feasible state trajectories in multi-contact robots and the corresponding control inputs. Typically, it takes a tremendous amount of time to solve the end-to-end trajectory generation problem using large-scale standard Nonlinear Programming (NLP). We propose a new sampling-based method together with a Partially Observable Markov Decision Process to break down the trajectory generation problem into tractable parts. In doing so, the number of decision variables is drastically reduced. As a result, we solve the optimization problem much faster than using existing NLP techniques. In addition, we incorporate reachability analysis tools for determining whether the planned trajectories are reachable and discard unfeasible trajectories during optimization. Because simplified models are frequently utilized in locomotion studies to generate walking patterns, planned contact locations may not be feasible due to model mismatch and robot constraints. In contrast, our method enables the generation of dynamically feasible trajectories to reach planned contact location considering full-body dynamics and realistic constraints. The proposed methods are applied to contact constrained manipulation and bipedal locomotion problems to enhance capabilities of robots maneuvering in complex environments without slip or loss of balance. Second, we explore the fundamentals of WBC and use this insight to push forward the capabilities of WBC approaches. One of the problems we explore is the verification of stability of legged robots under unknown external perturbations. In such cases, the closed-loop control system controlled by WBC approaches may become unstable if external perturbations are not properly analyzed with stability verification. To verify stability, we leverage the so-called Centroidal Dynamics of legged robots and a type of WBC dubbed Whole-Body Locomotion Control (WBLC). Using a feedback-linearized state-space model, we obtain appropriate feedback gains for WBC to make our robot stable and robust under perturbations. Another challenge of WBC stems from the reliance on classical feedback control theory. Classical PD control is unsuitable for a noisy system, therefore WBC cannot be directly applied to stochastic systems. Classical WBC approaches do not consider the covariance of the terminal states as constraints which is a more efficient way to control robots with precision. We propose a new control approach, called Hierarchical Covariance Control (HCC) to enforce covariance constraints. Our proposed HCC is a stochastic version of WBC to decrease task errors when uncertainty is substantial. The last improvement I explore regarding WBC is the employment of Model Predictive Control (MPC) instead of solving an instantaneous optimization problem, which cannot guarantee global optimality. As such, we consider longer receding time horizons for MPC, thus improving the tracking performance by reducing the accumulated error norm while executing hierarchical tasks. Overall, our research focuses on the end-to-end process spanning trajectory planning to feedback control enabling the generating of multi-contact and constrained dynamic motions of complex robots operating in realistic setups. The various contributions of this thesis are in the areas of computational efficiency for whole-body trajectory generation, robustness of WBC control algorithms, and significant improvements in trajectory tracking using WBC algorithms. We verify the proposed approaches both in simulations and real experiments using various robotic systems