Experimental Evaluation of a Perceptual Pipeline for Hierarchical Affordance Extraction.

The perception of affordances in unknown environments is an essential prerequisite for autonomous humanoid robots. In our previous work we developed a perceptual pipeline for the extraction of affordances for loco-manipulation actions based on a simplified representation of the environment starting from RGB-D camera images. The feasibility of this approach has been demonstrated in various examples in simulation as well as on real robotic platforms. The overall goal of the perceptual pipeline is to provide a robust and reliable perceptual mechanism for affordance-based action execution. In this work we evaluate the performance of the perceptual pipeline in combination with sensor systems other than RGB-D cameras, in order to utilize redundant sensor equipment of humanoid robots. This is particularly important when considering challenging scenarios where particular sensors are not applicable, e.g. due to intense sunlight or reflective surfaces. In this work we focus on stereo cameras and LIDAR laser scanners.