Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations

TL;DR

This paper demonstrates that real-world multi-objective evolutionary optimization can effectively adapt both the control and morphology of a four-legged robot to hardware limitations, achieving comparable performance across different speeds.

Contribution

It introduces a practical method for evolving robot control and morphology directly in the real world under hardware constraints, bridging the simulation-reality gap.

Findings

Evolution under hardware limitations results in adaptable morphologies.

Control and morphology co-evolve to maintain performance.

Feasible with relatively few evaluations in real-world settings.

Abstract

For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this real-world evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.