Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis

Human-Likeness Indicator for Robot Posture Control and Balance

Similarly to humans, humanoid robots require posture control and balance to walk and interact with the environment. In this work posture control in perturbed conditions is evaluated as a performance test for humanoid control. A specific performance indicator is proposed: the score is based on the comparison between the body sway of the tested humanoid standing on a moving surface and the sway produced by healthy subjects performing the same experiment. This approach is here oriented to the evaluation of a human-likeness. The measure is tested using a humanoid robot in order to demonstrate a typical usage of the proposed evaluation scheme and an example of how to improve robot control on the basis of such a performance indicator scoreComment: 16 pages, 5 Figures. arXiv admin note: substantial text overlap with arXiv:2110.1439

Biorobotics: Using robots to emulate and investigate agile animal locomotion

The graceful and agile movements of animals are difficult to analyze and emulate because locomotion is the result of a complex interplay of many components: the central and peripheral nervous systems, the musculoskeletal system, and the environment. The goals of biorobotics are to take inspiration from biological principles to design robots that match the agility of animals, and to use robots as scientific tools to investigate animal adaptive behavior. Used as physical models, biorobots contribute to hypothesis testing in fields such as hydrodynamics, biomechanics, neuroscience, and prosthetics. Their use may contribute to the design of prosthetic devices that more closely take human locomotion principles into account

A Variable Stiffness Actuator Module With Favorable Mass Distribution for a Bio-inspired Biped Robot

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio

An Overview on Principles for Energy Efficient Robot Locomotion

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied

Stabilization of bipedal walking based on compliance control

The embodiment of physical compliance in humanoid robots, inspired by biology, improves the robustness of locomotion in unknown environments. The mechanical implementation using elastic materials demands a further combination together with controlled compliance to make the intrinsic compliance more effective. We hereby present an active compliance control to stabilize the humanoid robots for standing and walking tasks. Our actively controlled compliance is achieved via admittance control using closed-loop feedback of the six axis force/torque sensors in the feet. The modeling and theoretical formulation are presented, followed by the simulation study. Further, the control algorithms were validated on a real humanoid robot COMAN with inherent compliance. A series of experimental comparisons were studied, including standing balancing against impacts, straight walking, and omni-directional walking, to demonstrate the necessity and the effectiveness of applying controlled compliance on the basis of physical elasticity to enhance compliant foot-ground interaction for the successful locomotion. All data from simulations and experiments related with the proposed controller and the performance are presented, analyzed, and discussed

Simulating a Flexible Robotic System based on Musculoskeletal Modeling

Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs

In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2 to 3 primitives) than kinematic patterns from on-ground locomotion (4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware