@phdthesis{8720560,
  abstract     = {{A better understanding of locomotion, and the processes that it involves, has potential benefits in our society. On one hand, it could help to design efficient legged robots, with better accessibil- ity to the different environments on the globe, whereas wheeled platforms remain generally limited on even terrains without ob- stacles. This could be used to build social robots or to deal with exploration and rescue operations in hazardous environments. On the other hand, improving the understanding of the locomotion mechanisms can also contribute to biological sciences and, in par- ticular, neurosciences. In this regard, studies on locomotion are a typical illustration of an embodied problem, which is often cited as a key concept to bridge the different scales in brain research, from the chemical and physical processes to the behavioral and psychological aspects.
To improve the agility and adaptability of robotic locomotion plat- forms, an appealing path is to use compliant structures and actua- tors rather than stiff elements. However, compliant and soft robots are not well suited for control with traditional computational ar- chitectures, generally directed towards centralized commands and exactness. New models, driven by data, or inspired by the broad locomotion abilities encountered within biological systems, create an opportunity to improve the state of the art in robotics. But they also raise questions on several fundamental aspects, limiting their maturity and their diffusion in the society. This dissertation tries to better investigate three of these research questions: how can we transfer knowledge from simulation to real robots, how does the mechanical compliance correlate with the locomotion performance and the controller complexity, and, thirdly, how can reflex-based control on compliant structures benefit from a stance correction mechanism taking its inspiration in the biological Cerebellum.
In the introduction and the state-of-the-art chapters, I identify these questions more precisely and provide an overview of the existing literature on the subject. The next chapter gives more de- tails on the control methodology used in this dissertation. Chapter 4 presents three different robot platforms to target the research questions: a simulated network of masses and springs (MSD struc- ture), a cheap passive compliant quadruped robot (Tigrillo), and a state-of-the-art quadruped robot with active compliant actuators (HyQ). The next four chapters expose the approach and discuss the results of the experimental trials conducted on these platforms, to formulate contributions in the domain. Finally, a conclusion is provided in the last chapter.
The contribution of this dissertation is manifold and five main topics are investigated throughout the manuscript. First, the re- cent progress in machine learning has led to impressive results for the locomotion of simulated creatures. However, mechanical compliance is not often considered in the work from this field and the difficulty to transfer trained parametric models from simula- tion to the real world has been highlighted in different research tracks. In this dissertation, I suggest an optimization procedure of the physics simulation model to reduce the difference between observations in simulation and the real world.
Secondly, an analysis of the state of the art in the field of robotics shows that compliance is not a straightforward defined concept, although it is generally linked to two physical parameters, damping and stiffness. I further analyze this relation through empirical analysis of non-linear robotic systems. They seem to indicate that the concept of resonance, only strictly defined in systems with second-order ordinary differential equations, could serve as a first approximation of the link between stiffness, damping, and optimal locomotion frequency. An investigation of this dependence is conducted on the MSD structures and the HyQ robot.

Thirdly, this dissertation presents a training method in two steps: a parameter optimization of open-loop biologically inspired models, followed by a supervised training of a feed-forward neural network, linking robot’s sensors and actuators, to reproduce the targets obtained in the first step in closed-loop. This architecture demon- strates the ability to learn a reflex-based locomotion model without the need for centralized control. The properties of this closed-loop dynamical system are investigated on the three different robotic platforms and the robustness against external disturbance is also discussed.
Fourthly, to connect this reflex-based model with biological obser- vations, I also compare two architectural hypotheses in a simple stability controller for a quadruped robot: one using an internal spatiotemporal representation of the body system, and the other based on afferent sensor signals from the lower limbs. I show that the first model performs better in maintaining a target locomotion frequency and resisting external disturbances, which corroborates biological observations conducted on the Cerebellum’s functioning.
All these items put together establish an ideal framework to fur- ther investigate the potential exchange of computation capacity between physical body and controller during locomotion, as ex- pected from the theory of morphological computation. A reflection on the matter constitutes the fifth contribution of this disserta- tion. The experiments on HyQ and the MSD networks promote another formulation of this phenomenon: although it is not pos- sible to confirm a transfer of computation per se in my trials, we observe that increased structural complexity and larger mechan- ical compliance contribute to the simplification of computational requirements in the controller and promote a more stable locomo- tion process against external disturbance.
In conclusion, this work contributes to better understanding the impact of mechanical compliance on the design and the tuning of a locomotion controller. The experimental results advocate in favor of the use of compliance in robotics, not only to improve performance but also to simplify the control, in association with generic and data-driven controllers. Furthermore, anchoring the architecture of these controllers into biological observations proves to be a source of inspiration for enhanced robots but also a way to test hypotheses and better understand the phenomena in action during human and animal locomotion.}},
  author       = {{Urbain, Gabriel}},
  isbn         = {{9789463555142}},
  keywords     = {{robotics,control,machine learning}},
  language     = {{eng}},
  pages        = {{XXXII, 237}},
  publisher    = {{Ghent University. Faculty of Engineering and Architecture}},
  school       = {{Ghent University}},
  title        = {{Biologically inspired locomotion of compliant robots}},
  year         = {{2021}},
}