Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

Abstract

This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morphologies.

From real-time adaptation to social learning in robot ecosystems

“…7 with the feedback weights w i fixed at their final learned values. Since stability cannot be guaranteed in the closed-loop case, we use a homeostatic feedback to stabilize the output level Husbands et al (2021) . We therefore adjust the LIF firing threshold b ( t ) if the feedback level is not close to the time-averaged teacher input E t [ z T ]: where is a moving average of the last 200 frames (3 s), b (0) = 1, η > 0 and k > 0.…”

“…This can be seen as an embodied version of the “dynamic attending” approach to beat perception, which was proposed using abstracted nonlinear oscillators ( Large and Jones, 1999 ). More broadly, this falls within the approach of exploiting self-organized nonlinear dynamics in order to generate complex behaviours ( Steingrube et al, 2010 ; Husbands et al, 2021 ).…”

Deformable Morphing and Multivariable Stiffness in the Evolutionary Robotics

“…Recently, Wang et al [65] presented a fully realized adaptive resource landscape with diploid three-gene robots presenting interacting roles of population dynamics, mutations, breeding, death, and birth. Eiben et al [66 -68] and Husbands et al [69] outlined a perspective on the future of evolutionary robotics and discussed a long-term vision regarding robots that evolve in the real world in 2021.…”

Mechanical arm(s) driven by Josephson junction circuit(s), mimicking the movement pattern of myriapods

“…To date, locomotion is an aspect of life that can't be neglected as technological advancement in this aspect related to robotics has expatiated a subbranch of the bio-inspired model called bio-inspired robotic locomotion mimicking biological organisms or systems [43,44]. Bio-robotics is created from a combination of mechanics, electronics, and bio-informatics, changing the narrative and quality of living [45][46][47][48].…”