Automating Damage Recovery in a Legged Robot

Autonomous robots are increasingly used in remote and hazardous environments, where automated recovery given damage to sensory-actuator systems would be extremely beneficial. Such robots must therefore have controllers that continue to function effectively given unexpected hardware malfunctions and damage. We evaluate various controller types (oscillatorstyle central pattern generators and artificial neural networks), for producing adaptable gait behaviors. These controller types are run for hexapod robot gait control in concert with the Intelligent Trial and Error (IT&E) and Map-Elites algorithm to maintain behavioral diversity. Specifically, we investigate the impact of behavior map-size in MAP-Elites (the first phase of the IT&E algorithm), in company with various controller types for multiple leg failures scenarios using a simulated hexapod robot. Results support previous work demonstrating a trade-off between adapted gait speed and controller adaptability across leg-damage scenarios, where map-size is crucial for generating behavioral diversity required for adaptation.