A Model-Based Control Approach for Locomotion of Biped Robots

In this research we aim at proposing a general novel walking method for locomotion of torque controlled robots. The method should be able to produce a wide range of speeds without requiring oﬀ-line optimizations and re-tuning of parameters. It should be capable of tolerating internal errors, noises and control delays as well as external disturbances such as pushes or roughness in the environment. We have a quadratic whole-body optimization which generates joint toques, given desired Cartesian accelerations of center of mass and feet. Using dynamics model of the robot inside this optimizer ensures compliance and better tracking, required for fast locomotion. We have simpliﬁed the model of robot to linear inverted pendulum and proposed diﬀerent planners which are other quadratic convex problems optimizing future behavior of the robot. These planners are in fact model predictive control which optimize the system either in continuous or discrete time domains. Fast libraries help us performing these calculations per time step and producing desired motion. With very few parameters to tune and no perception, our method shows notable robustness against strong external pushes, large terrain variations, internal noises, model errors and also delayed communication. Evident by various simulations in diﬀerent conditions, we can suggest our general method for walking control of a wide range of humanoid robots.

Towards Robust Bipedal Locomotion

Salman Faraji

Thanks to better actuator technologies and control algorithms, humanoid robots to date can perform a wide range of locomotion activities outside lab environments. These robots face various control challenges like high dimensionality, contact switches during locomotion and a floating-base nature which makes them fall all the time. A rich set of sensory inputs and a high-bandwidth actuation are often needed to ensure fast and effective reactions to unforeseen conditions, e.g., terrain variations, external pushes, slippages, unknown payloads, etc. State of the art technologies today seem to provide such valuable hardware components. However, regarding software, there is plenty of room for improvement. Locomotion planning and control problems are often treated separately in conventional humanoid control algorithms. The control challenges mentioned above are probably the main reason for such separation. Here, planning refers to the process of finding consistent open-loop trajectories, which may take arbitrarily long computations off-line. Control, on the other hand, should be done very fast online to ensure stability. In this thesis, we want to link planning and control problems again and enable for online trajectory modification in a meaningful way. First, we propose a new way of describing robot geometries like molecules which breaks the complexity of conventional models. We use this technique and derive a planning algorithm that is fast enough to be used online for multi-contact motion planning. Similarly, we derive 3LP, a simplified linear three-mass model for bipedal walking, which offers orders of magnitude faster computations than full mechanical models. Next, we focus more on walking and use the 3LP model to formulate online control algorithms based on the foot-stepping strategy. The method is based on model predictive control, however, we also propose a faster controller with time-projection that demonstrates a close performance without numerical optimizations. We also deploy an efficient implementation of inverse dynamics together with advanced sensor fusion and actuator control algorithms to ensure a precise and compliant tracking of the simplified 3LP trajectories. Extensive simulations and hardware experiments on COMAN robot demonstrate effectiveness and strengths of our method. This thesis goes beyond humanoid walking applications. We further use the developed modeling tools to analyze and understand principles of human locomotion. Our 3LP model can describe the exchange of energy between human limbs in walking to some extent. We use this property to propose a metabolic-cost model of human walking which successfully describes trends in various conditions. The intrinsic power of the 3LP model to generate walking gaits in all these conditions makes it a handy solution for walking control and gait analysis, despite being yet a simplified model. To fill the reality gap, finally, we propose a kinematic conversion method that takes 3LP trajectories as input and generates more human-like postures. Using this method, the 3LP model, and the time-projecting controller, we introduce a graphical user interface in the end to simulate periodic and transient human-like walking conditions. We hope to use this combination in future to produce faster and more human-like walking gaits, possibly with more capable humanoid robots.

EPFL2018

A Control and Optimization Framework for Robot Locomotion

Soha Pouya

Despite great efforts in designing legged robots, we are still far from the adaptivity, efficiency and robustness with which animals can move. Observations from successful biomimetic designs highlight the significant role of clever morphological design in achieving such performance. In addition, the important role of the controller in improving the locomotion performance is undoubtable. In fact, there is a complex link between morphology and motor control of locomoting systems. In animals’ locomotion, there is a large number of evidence showing the effective link between their morphology and functionality. Motivated by biological observations, similar questions can be posed from a robotics perspective: “what are the relative and complementary roles of robot control and morphology (similar to animal’s brain and body) in the generated locomotor behavior?”, and more pragmatically, “how can we take lessons from these observations to reduce the current gap between robots’ locomotion performance and what is observed in their biological counterparts?”. These are the fundamental questions that have motivated studies in this thesis. While empirical studies are essential to address aforementioned questions, they require simplifying contexts to yield insightful predictions and examinable theories. Both theoretical and physical models (i.e. simulations and robots) can significantly contribute in providing such insights. While robot experiments are essential to evaluate and explore the involved biological concept, they are usually time-consuming and costly. Hence, it would be advantageous if one could exploit the power of theoretical models for development of ideas and hypotheses before their implementation on the real robot. In this thesis, we aim at leveraging the strength of theoretical models to study the above-mentioned questions, namely, the relative role of control and morphology in improving the robot locomotion functionalities. To provide useful theoretical models, we take a step-wise approach, where we start from model-free methods and proceed with advancing the locomotion control by incorporating dynamics models at different levels of complexity. In particular, we develop a pool of robot models, starting from a simplified massless leg to a detailed dynamics model of the leg, to a 2D quadruped with spine joint and eventually to a 3D detailed dynamics model of a quadruped. Along with developing dynamics model of the robot, we devise a set of control and optimization techniques with the main goal of improving locomotion performance. There are several performance criteria to consider (such as energy efficiency, maneuverability, adaptivity to unknown terrains) and a key question is how to build the control architecture to hold a proper trade-off between these partially contradicting criteria. We take advantage of the developed dynamic models in both (i) improving the controller performance and (ii) implementing analytical tools to evaluate and compare different locomotion performance criteria. The central outcome of this thesis is a bio-inspired modeling, control, and optimization framework for legged robot locomotion, which is evaluated on a set of robots with different morphologies. In all cases, we explore how the locomotion performance can be improved by introducing new control techniques or be influenced by the morphological aspects (such as joint configuration and mass distribution). In particular, we show the effect of using more detailed dynamics model to improve the predictive performance of the simulations both for analyzing morphological factors and for robustness of the controller to internal (e.g. change in the inertial parameters) and external (e.g. walking on rough terrains) perturbations. The framework is generic and is used to model, control and analyze locomotion gaits for three compliant quadruped robots (Locomorph, Cheetah-Cub and Bobcat robots) and a modular robot (Roombots).

EPFL2013

Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality

Stéphane Bonardi, Auke Ijspeert, Rico Möckel, Soha Pouya, Alexander Spröwitz, Massimo Vespignani

The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time con- suming even if optimization techniques are applied. Control parameters can be found through optimization in two ways: (i) through online optimization where the performance of a robot is measured while trying different control parameters on the actual hardware and (ii) through offline optimization by simulating the robot’s behavior with the help of models of the robot and its environment. In this paper, we present a hybrid optimization method that combines the best properties of online and offline optimization to efficiently find locomotion gaits for arbitrary structures. In comparison to pure online optimization both the number of experiments using robotic hardware as well as the total time required for finding efficient locomotion gaits get highly reduced by running the major part of the optimization process in simulation using a cluster of processors. The presented example shows that even for robots with a low number of degrees of freedom the time required for optimization can be reduced by at least a factor of 2.5 to 30 depending on how extensive the search for optimized control parameters should be. Time for hardware experiments becomes minimal. More importantly gaits that can possibly damage the robotic hardware can be filtered before being tried. Yet in contrast to pure offline optimization we reach well matched behavior that allows a direct transfer of locomotion gaits from simulation to hardware. This is because through a meta-optimization we adapt not only the locomotion parameters but also the parameters for simulation models of the robot and environment allowing for a good matching of the behavior of simulated and hardware robot structures. We verify the proposed hybrid optimization method on a structure composed of two Roombots modules. Roombots are self-reconfigurable modular robots that can form arbitrary structures with many degrees of freedom through an integrated active connection mechanism.

2013