Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video

Mathematical Modelling and Control System Development of a Remote Controlled, IMU Stabilised Hexapod Robot

Walking robots are useful in search and rescue applications due to their ability to navigate uneven and complex terrain. A hexapod robot has been developed by the Robotics and Agents Research Lab at UCT, however multiple inadequacies have become evident. This work aims to produce a mathematical model of the hexapod and using this model, implement an effective control algorithm to achieve a smooth walking motion and overcome the original flaws. The mathematical model was integrated with the mechanical structure of the hexapod and controlled by a micro-controller. This micro-controller allows for a rapid start-up and low power consumption when compared to previous iterations of the hexapod. Using a path generation algorithm sets of foot positions and velocities are generated. Generating these points in real time allows for walking in any direction without any pre-defined foot positions. To enable attitude control of the hexapod body, an inertial measurement unit was added to the hexapod. By using a PID controller the IMU pitch and roll data was used to control a height offset of each foot of the hexapod, allowing for stabilisation of the hexapod body. An improved wireless remote control was developed to facilitate communication with a host computer. The remote system has a graphical user interface allowing for walking control and status information feedback, such as error information and current battery voltage. Walking tests have shown that the hexapod walks successfully with a smooth tripod gait using the path generation algorithm. Stabilisation tests have shown that the hexapod is capable of stabilising itself after a disturbance to its pitch and/or roll in ±2.5 seconds with a steady state error of ±0.001 radians. This proves that the hexapod robot can be controlled wirelessly while walking in any direction with a stabilised body. This is beneficial in search and rescue as the hexapod has a high degree of manoeuvrability to access areas too dangerous for rescuers to access. With cameras mounted on the stabilised body, it can be used to locate survivors in a disaster area and assist rescuers in recovering them with speed

Locomotion performance of hexapod robots on rough substrates and the influence of leg compliance

ABSTRACT OF THE THESISLocomotion performance of hexapod robots on rough substrates and the influence of leg compliancebyAmartya Bhattacharyya Master of Science in Engineering Sciences (Mechanical Engineering)University of California San Diego, 2019Professor Nicholas Gravish, ChairHexapod Robots are a complex system where six legs are connected to the main body which acts as a support frame. A lot of research has been performed in this field from the study of six legged insects to present day implementations where the robot uses its own decision making network. The motivation for this field are the various advantages that hexapedal robots provide like; Obstacle climbing capability, omnidirectional motion, variable geometry, stability, access to uneven terrain etc. At the same time they also have many disadvantages like low energy efficiency, low speeds, complexity of operation and design and especially a lot of attention has to be given to path and gait planning. Therefore, in this paper wexiiuse an open loop platform for our robot and test the performance on simulated rough substrates. Using the results we propose a compliant leg design which will improve the performance while maintaining the stability. We compare the new design with solid legs to quantify the gain. And also test for the shear force limits to make sure the design is ready to be tested on a robot for full length runs. With a goal to utilize the new design and simplify the requirements of complicated neural networks for gait planning

A Theory of Cheap Control in Embodied Systems

We present a framework for designing cheap control architectures for embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent's embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the classical non-embodied universal approximation. To exemplify our approach, we present a detailed quantitative case study for policy models defined in terms of conditional restricted Boltzmann machines. In contrast to non-embodied universal approximation, which requires an exponential number of parameters, in the embodied setting we are able to generate all possible behaviors with a drastically smaller model, thus obtaining cheap universal approximation. We test and corroborate the theory experimentally with a six-legged walking machine. The experiments show that the sufficient controller complexity predicted by our theory is tight, which means that the theory has direct practical implications. Keywords: cheap design, embodiment, sensorimotor loop, universal approximation, conditional restricted Boltzmann machineComment: 27 pages, 10 figure

Application of fractional calculus in the system modelling and control

Fractional Calculus (FC) goes back to the beginning of the theory of differential calculus. Nevertheless, the application of FC just emerged in the last two decades, due to the progress in the area of chaos that revealed subtle relationships with the FC concepts. In the field of dynamical systems theory some work has been carried out but the proposed models and algorithms are still in a preliminary stage of establishment. Having these ideas in mind, the paper discusses a FC perspective in the study of the dynamics and control of several systems.N/