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

Multi-physics Modelling of a Compliant Humanoid Robot

In this paper, we discuss some very important features for getting exploitable simulation results for multibody systems, relying on the example of a humanoid robot. First, we provide a comparison of simulation speed and accuracy for kinematics modeling relying on relative vs. absolute coor- dinates. This choice is particularly critical for mechanisms with long serial chains (e.g. legs and arms). Compliance in the robot actuation chain is also critical to enhance the robot safety and en- ergy efficiency, but makes the simulator more sensitive to modeling errors. Therefore, our second contribution is to derive the full electro-mechanical model of the inner dynamics of the compliant actuators embedded in our robot. Finally, we report our reasoning for choosing an appropriate contact library. The recommended solution is to couple our simulator with an open-source contact library offering both accurate and fast full-body contact modeling

Zero-Moment Point on a bipedal robot under bio-inspired walking control

Humanoid robots are currently still far from reaching the impressive human walking capabilities. Among the different methods used to design walking controllers, those based on the Zero-Moment Point (ZMP) criterion are among the most popular, even if they induce intrinsic limitations in terms of energy consumption and robustness. In parallel, bio-inspired controllers are emerging. They overcome the ZMP-based limitations, but still miss robust stabilization rules to be validated on real robots. This contribution studies how to efficiently compute the ZMP in realtime on a robot walking with bio-inspired control rules, in order to detect when the robot stability is compromised

Biped gait controller for large speed variations, combining reflexes and a central pattern generator in a neuromuscular model

Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole

Bio-inspired controller achieving forward speed modulation with a 3D bipedal walker

Despite all the effort devoted to generating locomotion algorithms for bipedal walkers, robots are still far from reaching the impressive human walking capabilities, for instance regarding robustness and energy consumption. In this paper, we have developed a bio-inspired torque-based controller supporting the emergence of a new generation of robust and energy-efficient walkers. It recruits virtual muscles driven by reflexes and a central pattern generator, and thus requires no computationally intensive inverse kinematics or dynamics modeling. This controller is capable of generating energy-efficient and human-like gaits (both regarding kinematics and dynamics) across a large range of forward speeds, in a 3D environment. After a single off-line optimization process, the forward speed can be continuously commanded within this range by changing high-level parameters, as linear or quadratic functions of the target speed. Sharp speed transitions can then be achieved with no additional tuning, resulting in immediate adaptations of the step length and frequency. In this paper, we particularly embodied this controller on a simulated version of COMAN, a 95 cm tall humanoid robot. We reached forward speed modulations between 0.4 and 0.9 m/s. This covers normal human walking speeds once scaled to the robot size. Finally, the walker demonstrated significant robustness against a large spectrum of unpredicted perturbations: facing external pushes or walking on altered environments, such as stairs, slopes, and irregular ground

Bio-inspired walking for humanoid robots using feet with human-like compliance and neuromuscular control

The human foot plays a key role in human walking providing, among others, body support and propulsion, stability of the movement and impact absorption. These fundamental functionalities are accomplished by an extraordinarily rich bio-mechanical design. Nonetheless, humanoid robots follow different approaches to walk, hence, they generally implement rigid feet. In this study, we target the gap existing between the human foot and traditional humanoid-robot feet. More specifically, we evaluate the resulting advantages and draw-backs by implementing on a humanoid robot some of the properties and functionalities embedded in the human foot. To this end, we extract the physical characteristics of a prosthetic foot to develop a human-like foot model. This foot model is systematically tested in simulation in human-like walking tasks on flat ground and on uneven terrain. The movement of the limbs is generated by a muscle-reflex controller based on a simplified model of the human limbs. The gait features and the walking stability are evaluated for the human-like foot and compared with the results produced using rigid feet

Neuromuscular model achieving speed control and steering with a 3D bipedal walker

Nowadays, very few humanoid robots manage to travel in our daily environments. This is mainly due to their limited locomotion capabilities, far from the human ones. Recently, we developed a bio-inspired torque-based controller recruiting virtual muscles driven by reflexes and a central pattern generator. Straight walking experiments were obtained in a 3D simulation environment, resulting in the emergence of human-like and robust gait patterns, with speed modulation capabilities. In this paper, we extend this model, in order to control the steering direction and curvature. Based on human turning strategies, new control pathways are introduced and optimized to reach the sharpest possible turns. In sum, tele-operated motions can be achieved through the control of two scalar inputs (i.e. forward speed and heading). This is particularly relevant for steering the robot on-line, and navigating in cluttered environments. Finally, the biped demonstrated significant robustness during blind walking experiments

Bio-inspired balance controller for a humanoid robot

Humanoid robots are gaining much interest nowadays. This is partly motivated by the ability of such robots to replace humans in dangerous environments being specifically designed for humans, such as man-made or natural disaster scenarios. However, existing robots are far from reaching human skills regarding the robustness to external perturbations required for such tasks, although torque-controlled and even bio-inspired robots hold new promises for research. A humanoid robot robustly interacting with its environment should be capable of handling highly uncertain ground structures, collisions, and other external perturbations. In this paper, a 3D bio-inspired balance controller is developed using a virtual lower limbs musculoskeletal model. An inverse muscular model that transforms the desired torque patterns into muscular stimulations closes the gap between traditional and bio-inspired controllers. The main contribution consists in developing a neural controller that computes the muscular stimulations driving this musculoskeletal model. This neural controller exploits the inverse model output to progressively learn the appropriate muscular stimulations for rejecting disturbances, without relying on the inverse model anymore. Two concurrent approaches are implemented to perform this autonomous learning: a cerebellar model and a support vector regression algorithm. The developed methods are tested in the Robotran simulation environment with COMAN, a compliant child-sized humanoid robot. Results illustrate that - at the end of the learning phase - the robot manages to reject perturbations by performing a full-body compensation requiring neither to solve an inverse dynamic model nor to get force measurement. Muscular stimulations are directly generated based on the previously learned perturbations

Simplification of the Hill Muscle Model Computation for Real-Time Walking Controllers with Large Time Steps

Bio-inspired controllers are emerging as a promising way to implement dynamic walking. In this study, we implemented the one proposed by Geyer & Herr (2010), relying on reflex-controlled virtual Hill muscles. In this model, muscles’ state is determined by the length (lce) of their active, contractile element. However, its update rate is governed by a stiff and strongly non-linear state equation, thus requiring a small integration time step