  In this paper, we provide details of a robotic system that can automate the
task of picking and stowing objects from and to a rack in an e-commerce
fulfillment warehouse. The system primarily comprises of four main modules: (1)
Perception module responsible for recognizing query objects and localizing them
in the 3-dimensional robot workspace; (2) Planning module generates necessary
paths that the robot end- effector has to take for reaching the objects in the
rack or in the tote; (3) Calibration module that defines the physical workspace
for the robot visible through the on-board vision system; and (4) Gripping and
suction system for picking and stowing different kinds of objects. The
perception module uses a faster region-based Convolutional Neural Network
(R-CNN) to recognize objects. We designed a novel two finger gripper that
incorporates pneumatic valve based suction effect to enhance its ability to
pick different kinds of objects. The system was developed by IITK-TCS team for
participation in the Amazon Picking Challenge 2016 event. The team secured a
fifth place in the stowing task in the event. The purpose of this article is to
share our experiences with students and practicing engineers and enable them to
build similar systems. The overall efficacy of the system is demonstrated
through several simulation as well as real-world experiments with actual
robots.
