Robots are already becoming commonplace for tasks which are dirty, dull and dangerous.  However, current technology limits tasks to be performed by either humans or robots in isolation. In the near future, tedious tasks will no longer be done solely by humans or robots, but will be completed by human-robot teams. In today’s factories, significant resources have been invested into complex, strong and agile robotic arms; however, due to the lack of their spatial awareness and difficulty in programming them, they are not easily adaptable to new tasks.  In addition, humans are prevented from moving into the work envelope while the machines are running, and so robots are generally left to tackle the dangerous tasks on the factory floor with poor or no sensing capability. In order to revolutionize production with the least cost possible to the factory, these arms must be retrofitted with a fairly inexpensive and adaptable sensing and control system.  We have developed a low cost control system that can be adapted to any arm which is based off of a commonly available 3D sensing system, such as the Microsoft Kinect (a consumer-grade RGB-D camera available for $100 that has a resolution of approximately 2mm at 1m to 2.5cm at 3m).  Use of such a sensor would allow an automated industrial manipulator arm to not only grasp and articulate moving objects alongside human beings in any environment, but also enable rapid reprogramming for new spatially oriented tasks, regardless of lighting conditions.  Because the depth camera collects diffracted infrared beam data and correlates it to each pixel in a color image it can be used in any indoor environment without the need for special equipment. Using simple image-processing techniques on the images from the depth camera, we have created a working proof-of-concept prototype that can recognize uniquely shaped objects moving on a 48 by 14 inch conveyor belt.  The system demonstrated its ability to recognize, grasp, and manipulate pieces to play a game of Tetris, making sure to optimize the position and orientation of each piece as it was detected. We are confident this technology can be applied to other applications, such as sorting waste products, organizing nuts and bolts, and any other task involving sorting of unique objects even if moving.