Body randomization reduces the sim-to-real gap for compliant quadruped locomotion

Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot

Morphological properties of mass-spring networks for optimal locomotion learning

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size

Populations of spiking neurons for reservoir computing : closed loop control of a compliant quadruped

Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing frame- work. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spik- ing neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot

Stance Control Inspired by Cerebellum Stabilizes Reflex-Based Locomotion on HyQ Robot

Advances in legged robotics are strongly rooted in animal observations. A clear illustration of this claim is the generalization of Central Pattern Generators (CPG), first identified in the cat spinal cord, to generate cyclic motion in robotic locomotion. Despite a global endorsement of this model, physiological and functional experiments in mammals have also indicated the presence of descending signals from the cerebellum, and reflex feedback from the lower limb sensory cells, that closely interact with CPGs. To this day, these interactions are not fully understood. In some studies, it was demonstrated that pure reflex-based locomotion in the absence of oscillatory signals could be achieved in realistic musculoskeletal simulation models or small compliant quadruped robots. At the same time, biological evidence has attested the functional role of the cerebellum for predictive control of balance and stance within mammals. In this paper, we promote both approaches and successfully apply reflex-based dynamic locomotion, coupled with a balance and gravity compensation mechanism, on the state-of-art HyQ robot. We discuss the importance of this stability module to ensure a correct foot lift-off and maintain a reliable gait. The robotic platform is further used to test two different architectural hypotheses inspired by the cerebellum. An analysis of experimental results demonstrates that the most biologically plausible alternative also leads to better results for robust locomotion

Aspectos de la desaparición. Calles y subterráneos

This essay discusses issues related to disappearances in urban space, in par- ticular cases that affect streets and subways, the mismatched equivalences of lines on the surface of urban space and what lays underground. Taking as a point of departure David Pike’s concept of threshold, which is key to defining a topography of the “vertical city”, a reading of plans and literary texts and films is proposed. This will illustrate the ways in which surface and other underground spaces overlap and the many differences that exist

Biologically inspired locomotion of compliant robots

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

Mobility estimation for Langevin dynamics using control variates

The scaling of the mobility of two-dimensional Langevin dynamics in a periodic potential as the friction vanishes is not well understood for non-separable potentials. Theoretical results are lacking, and numerical calculation of the mobility in the underdamped regime is challenging because the computational cost of standard Monte Carlo methods is inversely proportional to the friction coefficient, while deterministic methods are ill-conditioned. In this work, we propose a new variance-reduction method based on control variates for efficiently estimating the mobility of Langevin-type dynamics. We provide bounds on the bias and variance of the proposed estimator, and illustrate its efficacy through numerical experiments, first in simple one-dimensional settings and then for two-dimensional Langevin dynamics. Our results corroborate previous numerical evidence on the scaling of the mobility in the low friction regime for a simple non-separable potential

Les dysphasies: Pré-requis non linguistiques à l’acquisition du langage :détection de régularités

info:eu-repo/semantics/publishe