  Venipucture is a common step in clinical scenarios, and is with highly
practical value to be automated with robotics. Nowadays, only a few on-shelf
robotic systems are developed, however, they can not fulfill practical usage
due to varied reasons. In this paper, we develop a compact venipucture robot --
VeniBot, with four parts, six motors and two imaging devices. For the
automation, we focus on the positioning part and propose a Dual-In-Dual-Out
network based on two-step learning and two-task learning, which can achieve
fully automatic regression of the suitable puncture area and angle from
near-infrared(NIR) images. The regressed suitable puncture area and angle can
further navigate the positioning part of VeniBot, which is an important step
towards a fully autonomous venipucture robot. Validation on 30
VeniBot-collected volunteers shows a high mean dice coefficient(DSC) of 0.7634
and a low angle error of 15.58{\deg} on suitable puncture area and angle
regression respectively, indicating its potentially wide and practical
application in the future.
