Traversability on a simple humanoid: What did I just trip over?

The notion of affordance has taken the attention of roboticists in recent years. Previously we had used this concept to learn and perceive the traversability of a mobile robot platform. In this paper, we have shown how a simplistic humanoid robot equipped with time-of-flight ultrasonic sensor can learn traversability affordance. In addition to this, we have demonstrated it can infer how sensory data history affect this affordance by merging previously sensed data with the current data via a sliding data window concatenating recent history of sensor activity. This sliding window approach improves the performance of the system in the cases where the object is invisible at the time of collision. We performed several experiments in which the robot attained the ability to generalize what it has already learned by performing the move forward behavior robustly in cluttered environments with novel objects albeit noisy range measurements.