Simulated visually-guided paw placement during quadruped locomotion

Autonomous adaptive locomotion over irregular terrain is one important topic in robotics research. In this article, we focus on the development of a quadruped locomotion controller able to generate locomotion and reaching visually acquired markers. The developed controller is modeled as discrete, sensory driven corrections of a basic rhythmic motor pattern for locomotion according to visual information and proprioceptive data, that enables the robot to reach markers and only slightly perturb the locomotion movement. This task involves close-loop control and we will thus particularly focus on the essential issue of modeling the interaction between the central nervous system and the peripheral information in the locomotion context. This issue is crucial for autonomous and adaptive control, and has received little attention so far. Trajectories are online modulated according to these feedback pathways thus achieving paw placement. This modeling is based on the concept of dynamical systems whose intrinsic robustness against perturbations allows for an easy integration of sensory-motor feedback and thus for closed-loop control. The system is demonstrated on a simulated quadruped robot which online acquires the visual markers and achieves paw placement while locomotes

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Learning and Transfer of Modulated Locomotor Controllers

We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor primitives by training on simple tasks. This pre-trained module is fixed and connected to a low-frequency, high-level "cortical" network, with access to all sensors, which drives behavior by modulating the inputs to the spinal network. Where a monolithic end-to-end architecture fails completely, learning with a pre-trained spinal module succeeds at multiple high-level tasks, and enables the effective exploration required to learn from sparse rewards. We test our proposed architecture on three simulated bodies: a 16-dimensional swimming snake, a 20-dimensional quadruped, and a 54-dimensional humanoid. Our results are illustrated in the accompanying video at https://youtu.be/sboPYvhpraQComment: Supplemental video available at https://youtu.be/sboPYvhpra

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5

Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

Millirobots are a promising robotic platform for many applications due to their small size and low manufacturing costs. Legged millirobots, in particular, can provide increased mobility in complex environments and improved scaling of obstacles. However, controlling these small, highly dynamic, and underactuated legged systems is difficult. Hand-engineered controllers can sometimes control these legged millirobots, but they have difficulties with dynamic maneuvers and complex terrains. We present an approach for controlling a real-world legged millirobot that is based on learned neural network models. Using less than 17 minutes of data, our method can learn a predictive model of the robot's dynamics that can enable effective gaits to be synthesized on the fly for following user-specified waypoints on a given terrain. Furthermore, by leveraging expressive, high-capacity neural network models, our approach allows for these predictions to be directly conditioned on camera images, endowing the robot with the ability to predict how different terrains might affect its dynamics. This enables sample-efficient and effective learning for locomotion of a dynamic legged millirobot on various terrains, including gravel, turf, carpet, and styrofoam. Experiment videos can be found at https://sites.google.com/view/imageconddy

Brake Motion Control for Quadruped Hopping Robot by Using Reference Height Control System

In this paper, the generation of brake motion control for our developed quadruped hopping robot while moving on two dimensional spaces by jumping continuously is discussed. The braking motion method which is approached is by applying the reference height control system to create the differences of front leg and back leg while making moving performance and correct the body posture which has inclined to make the quadruped hopping robot jump vertically while braking performances. On the other hand, this approached method can be used as the collision-avoidance behavior for the quadruped hopping robot. The MATLAB/Simulink model is used in order to conduct the pattern generation of quadruped hopping robot. As the result, effectiveness of approach method is confirmed to generate brake motion control of quadruped hopping robot while making continuous jumping vertically. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserve

Moving Control of Quadruped Hopping Robot Using Adaptive CPG Networks

This paper describes the moving control using the adaptive Central Pattern Generators (CPGs) including motor dynamic models for our developed quadruped hopping robot. The CPGs of each leg is interconnected with each other and by setting their coupling parameters can act as the flexible oscillators of each leg and adjust the hopping height of each leg to require stable hopping motion. The formation of the CPG networks are suitable not only to generate the continuous jumping motion but also can generate the moving motion in twodimensional, respectively. We also propose the reference height control system which including the maximum hopping height detector and Proportional Integral (PI)controller to achieve the reference jumping height. By using the proposed method, the hopping height of each leg can be control independently in order to make the posture of robot’s body incline ahead and move forward. We create MATLAB/Simulink model to conduct various types of experiments and confirmed the effectiveness of our proposed CPG model including the reference height control system to generate the stable moving performance while jumping continuously