The role of compliance in humans and humanoid robots locomotion

We build robots that are meant to look and work like humans, with humans, inspired by humans. But many are the human characteristics that we have not yet understood, as humans are highly complex systems. One fundamental characteristic is compliance, which characterizes human movements. If our body was completely rigid, we would not be able to climb up trees or walk on mountainous paths as easily as we do. But despite being inspired to be a copy of human beings, humanoid robots had rigid links connected with rigid joints since their first appearance. It is only recently that they started to be more “human-like”, with the development of compliant actuators. In this thesis the objective is to analyze of the role of compliance in human walking and in humanoid robots motions. We model both the human body and humanoid robots as rigid multi-body systems. Both systems are highly redundant, reason for which optimization represents an essential tool to achieve our goals. In particular, we adopt optimal control approaches. In many state of the art compliant walking mechanisms, compliance is introduced at joint level by means of elastic components with constant stiffness, due to the difficulty of varying stiffness and the considerable dimensions of currently available variable stiffness actuators. This is the reason for which many studies focused on finding constant joint stiffness during human walking. However, biomechanics studies have shown that stiffness changes in human joints during movements. The questions we want to address are therefore: how does stiffness modulate during human walking and what is the influence of such modulations on the gait? To answer these questions, we used walking motions from motion capture data and a 2D dynamic model of the human body, where the actuation of the leg joints are modeled with torsional springs and bi-articular coupling springs with variable stiffness. We computed the stiffness profiles of these springs, which showed how stiffness changes over the walking cycle and can also assume big values, contrasting with many state of the art walking mechanisms. We proceeded by analyzing how walking gaits are modified if the stiffness modulation is reduced. This further step showed that the original walking gait could be approximated in unconstrained walking scenarios such as level ground and slopes but not in constraint ones as stairs. This result demonstrated the importance of stiffness modulation during walking and can serve for future compliant actuators design. There are several existing humanoid robots with compliant actuators. Among these, the iCub is a widely spreaded advanced research humanoid that has recently acquired legs with Series Elastic Actuators (SEA). The reduced version of it, HeiCub, was delivered to Heidelberg University by the end of 2014 and is the robot used in this thesis. We first analyzed the motion of squatting. The problem is formulated as an optimal control problem where only the three pitch joints of the legs are considered active and the whole-body dynamics of the robot is used. Squat motions for different objective functions are generated for the robot with and without the use of SEA. A step further is taken in using all the actuated degrees of freedom of the robot to generate push recovery motions with the same approach, also considering the SEA. As there is a lack of literature and experiments of iCub walking, for this complex task we aimed at exploiting the capabilities of HeiCub by measuring its walking performances. We used the table cart model to generate walking trajectories on level ground, slope and stairs, which have never been achieved before by other iCub robots. In this way we could gain details of the platform that were unknown beforehand that are fundamental to be used in future optimal control formulations. Thanks to this study, future developments of walking control frameworks for the iCub family robots have now a point of reference

Instantaneous Momentum-Based Control of Floating Base Systems

In the last two decades a growing number of robotic applications such as autonomous drones, wheeled robots and industrial manipulators started to be employed in several human environments. However, these machines often possess limited locomotion and/or manipulation capabilities, thus reducing the number of achievable tasks and increasing the complexity of robot-environment interaction. Augmenting robots locomotion and manipulation abilities is a fundamental research topic, with a view to enhance robots participation in complex tasks involving safe interaction and cooperation with humans. To this purpose, humanoid robots, aerial manipulators and the novel design of flying humanoid robots are among the most promising platforms researchers are studying in the attempt to remove the existing technological barriers. These robots are often modeled as floating base systems, and have lost the assumption -- typical of fixed base robots -- of having one link always attached to the ground. From the robot control side, contact forces regulation revealed to be fundamental for the execution of interaction tasks. Contact forces can be influenced by directly controlling the robot's momentum rate of change, and this fact gives rise to several momentum-based control strategies. Nevertheless, effective design of force and torque controllers still remains a complex challenge. The variability of sensor load during interaction, the inaccuracy of the force/torque sensing technology and the inherent nonlinearities of robot models are only a few complexities impairing efficient robot force control. This research project focuses on the design of balancing and flight controllers for floating base robots interacting with the surrounding environment. More specifically, the research is built upon the state-of-the-art of momentum-based controllers and applied to three robotic platforms: the humanoid robot iCub, the aerial manipulator OTHex and the jet-powered humanoid robot iRonCub. The project enforces the existing literature with both theoretical and experimental results, aimed at achieving high robot performances and improved stability and robustness, in presence of different physical robot-environment interactions

Thermal Recovery of Multi-Limbed Robots with Electric Actuators

The problem of finding thermally minimizing configurations of a humanoid robot to recover its actuators from unsafe thermal states is addressed. A first-order, data-driven, effort based, thermal model of the robots actuators is devised, which is used to predict future thermal states. Given this predictive capability, a map between configurations and future temperatures is formulated to find what configurations, subject to valid contact constraints, can be taken now to minimize future thermal states. Effectively, this approach is a realization of a contact-constrained thermal inverse-kinematics (IK) process. Experimental validation of the proposed approach is performed on the NASA Valkyrie robot hardware

Passive Motion Paradigm: An Alternative to Optimal Control

In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the “degrees of freedom (DoFs) problem,” the common core of production, observation, reasoning, and learning of “actions.” OCT, directly derived from engineering design techniques of control systems quantifies task goals as “cost functions” and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative “softer” approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that “animates” the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints “at runtime,” hence solving the “DoFs problem” without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of “potential actions.” In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures

UBOT-7: THE DESIGN OF A COMPLIANT DEXTEROUS MOBILE MANIPULATOR

This thesis presents the design of uBot-7, the latest version of a dexterous mobile manipulator. This platform has been iteratively developed to realize a high performance-to-cost dexterous whole body manipulator with respect to mobile manipulation. The semi-anthropomorphic design of the uBot is a demonstrated and functional research platform for developing advanced autonomous perception, manipulation, and mobility tasks. The goal of this work is to improve the uBot’s ability to sense and interact with its environment in order to increase the platforms capability to operate dexterously, through the incorporation of joint torque feedback, and safely, through the implementation of passive and active compliance. This is accomplished through incorporating series elastic actuators in its arms and torso joints, improving the mechanical design to reduce backlash, and incorporating impedance controllers in the robot. The focus of this thesis is the development of the mechanical, sensor, and controller design for the uBot-7 platform. An impedance controller is developed and evaluated on a bench top prototype series elastic actuator

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Learning-based methods for planning and control of humanoid robots

Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3