Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

This paper surveys the developments of the last 20 years in the field of central pattern
generator (CPG) inspired locomotion control, with particular emphasis on the fast emerging …

The reality gap, which often makes controllers evolved in simulation inefficient once
transferred onto the physical robot, remains a critical issue in evolutionary robotics (ER). We …

Despite the recent successes of deep reinforcement learning, teaching complex motor skills
to a physical robot remains a hard problem. While learning directly on a real system is …

Natural lifeforms specialize to their environmental niches across many levels, from low-level
features such as DNA and proteins, through to higher-level artefacts including eyes, limbs …

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize
controllers for real autonomous robots based only on a task specification. While a number of …

The reality gap, that often makes controllers evolved in simulation inefficient once
transferred onto the real system, remains a critical issue in Evolutionary Robotics (ER); it …

Evolutionary robotics (ER) is often viewed as the application of a family of black-box
optimization algorithms—evolutionary algorithms—to the design of robots, or parts of robots …

The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-
line automatic design of control software for robot swarms, as well as for single robots. It is …

Reinforcement learning (RL) can be a tool for designing policies and controllers for robotic
systems. However, the cost of real-world samples remains prohibitive as many RL …