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

Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks

Living organisms can act autonomously because biological neural networks process the environmental information in continuous time. Therefore, living organisms have inspired many applications of autonomous control to small-sized robots. In this chapter, a small-sized robot is controlled by a hardware artificial neural network (ANN) without software programs. Previously, the authors constructed a multilegged walking robot. The link mechanism of the limbs was designed to reduce the number of actuators. The current paper describes the basic characteristics of hardware ANNs that generate the gait for multilegged robots. The pulses emitted by the hardware ANN generate oscillating patterns of electrical activity. The pulse-type hardware ANN model has the basic features of a class II neuron model, which behaves like a resonator. Thus, gait generation by the hardware ANNs mimics the synchronization phenomena in biological neural networks. Consequently, our constructed hardware ANNs can generate multilegged robot gaits without requiring software programs

An Overview of Legged Robots

The objective of this paper is to present the evolution and the state-of-theart in the area of legged locomotion systems. In a first phase different possibilities for mobile robots are discussed, namely the case of artificial legged locomotion systems, while emphasizing their advantages and limitations. In a second phase an historical overview of the evolution of these systems is presented, bearing in mind several particular cases often considered as milestones on the technological and scientific progress. After this historical timeline, some of the present day systems are examined and their performance is analyzed. In a third phase are pointed out the major areas for research and development that are presently being followed in the construction of legged robots. Finally, some of the problems still unsolved, that remain defying robotics research, are also addressed.N/

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 195

This bibliography lists 148 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1979

Axel: A Minimalist Tethered Rover for Exploration of Extreme Planetary Terrains

Recent scientific findings suggest that some of the most interesting sites for future exploration of planetary surfaces lie in terrains that are currently inaccessible to conventional robotic rovers. To provide robust and flexible access to these terrains, we have been developing Axel, the robotic rover. Axel is a lightweight two-wheeled vehicle that can access steep terrains and negotiate relatively large obstacles because of its actively managed tether and novel wheel design. This article reviews the Axel system and focuses on those system components that affect Axel's steep terrain mobility. Experimental demonstrations of Axel on sloped and rocky terrains are presented

Body and leg coordination for omni-directional walking in rough terrain

International Conference on Climbing and Walking Robots (CLAWAR), 2000, Madrid (España)In this paper, we address the problem of moving a legged robot in an unknown environment according to externally provided driving commands assuming an arbitrary initial position of legs. This general problem is decomposed in three subproblems: first, decide when to lift a given leg, second, choose where to move lifted legs, and third, coordinate body and leg movements to make the robot advance in the desired direction. To solve this third problem, we extend the posture control mechanism introduced in (1) so that body movements are limited to the desired trajectory. The resulting controller performs smooth transitions between different trajectories and can cope with irregular terrain where valid footholds are scarce.This work was supported by the project 'Navegación basada en visión de robots autónomos en entornos no estructurados.' (070-724).Peer Reviewe

Design Principles for a Family of Direct-Drive Legged Robots

This letter introduces Minitaur, a dynamically running and leaping quadruped, which represents a novel class of direct-drive (DD) legged robots. We present a methodology that achieves the well-known benefits of DD robot design (transparency, mechanical robustness/efficiency, high-actuation bandwidth, and increased specific power), affording highly energetic behaviors across our family of machines despite severe limitations in specific force. We quantify DD drivetrain benefits using a variety of metrics, compare our machines\u27 performance to previously reported legged platforms, and speculate on the potential broad-reaching value of “transparency” for legged locomotion. For more information: Kod*lab