  We envision that in the near future, humanoid robots would share home space
and assist us in our daily and routine activities through object manipulations.
One of the fundamental technologies that need to be developed for robots is to
enable them to detect objects and recognize them for effective manipulations
and take real-time decisions involving those objects. In this paper, we
describe a novel architecture that enables multiple low-compute NAO robots to
perform real-time detection, recognition and localization of objects in its
camera view and take programmable actions based on the detected objects. The
proposed algorithm for object detection and localization is an empirical
modification of YOLOv3, based on indoor experiments in multiple scenarios, with
a smaller weight size and lesser computational requirements. Quantization of
the weights and re-adjusting filter sizes and layer arrangements for
convolutions improved the inference time for low-resolution images from the
robot s camera feed. YOLOv3 was chosen after a comparative study of bounding
box algorithms was performed with an objective to choose one that strikes the
perfect balance among information retention, low inference time and high
accuracy for real-time object detection and localization. The architecture also
comprises of an effective end-to-end pipeline to feed the real-time frames from
the camera feed to the neural net and use its results for guiding the robot
with customizable actions corresponding to the detected class labels.

Computer Vision and Pattern Recognition;Machine Learning;Robotics;Machine Learning