Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design
This work addresses robot morphology optimization for specific environments, but it is incremental as it builds on past research on evolving robot components.
The study compared direct (Bezier splines) and indirect (CPPN-NEAT) representations for designing 3D-printed hexapod robot legs, finding that the indirect method allowed greater design exploration and led to improved fitness across three simulated environments.
We compare two representations used to define the morphology of legs for a hexapod robot, which are subsequently 3D printed. A leg morphology occupies a set of voxels in a voxel grid. One method, a direct representation, uses a collection of Bezier splines. The second, an indirect method, utilises CPPN-NEAT. In our first experiment, we investigate two strategies to post-process the CPPN output and ensure leg length constraints are met. The first uses an adaptive threshold on the output neuron, the second, previously reported in the literature, scales the largest generated artefact to our desired length. In our second experiment, we build on our past work that evolves the tibia of a hexapod to provide environment-specific performance benefits. We compare the performance of our direct and indirect legs across three distinct environments, represented in a high-fidelity simulator. Results are significant and support our hypothesis that the indirect representation allows for further exploration of the design space leading to improved fitness.