Albert Causo

TL;DR: An overview of the inaugural Amazon Picking Challenge is presented along with a summary of a survey conducted among the 26 participating teams, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task.

TL;DR: The effectiveness of using robots in studies published within the last decade is assessed as having four sub-factors--the study type done by the researcher, the influence of the robots on the behaviour and development of students, the perception of stakeholders (parents, educators and children) about the robots, and the importance of design or robot appearance.

TL;DR: In the first Amazon picking challenge as mentioned in this paper, 26 international teams designed robotic systems that competed to retrieve items from warehouse shelves, and learned about each team's background, mechanism design, perception apparatus, planning and control approach.

TL;DR: This paper proposes a set of performance metrics selected in terms of actual field use as a solution to clarify the important technologies in bin picking and uses the selected metrics to compare the four original robot systems, which achieved the best performance in the Stow task of the Amazon Robotics Challenge 2017.

TL;DR: A wearable system that measures orientation and corrects arm posture using vibrotactile actuators and evaluates user posture with respect to a reference and gives feedback in the form of vibration patterns is proposed.