  LEGO is a well-known platform for prototyping pixelized objects. However,
robotic LEGO prototyping (i.e. manipulating LEGO bricks) is challenging due to
the tight connections and accuracy requirement. This paper investigates safe
and efficient robotic LEGO manipulation. In particular, this paper reduces the
complexity of the manipulation by hardware-software co-design. An end-of-arm
tool (EOAT) is designed, which reduces the problem dimension and allows large
industrial robots to easily manipulate LEGO bricks. In addition, this paper
uses evolution strategy to safely optimize the robot motion for LEGO
manipulation. Experiments demonstrate that the EOAT performs reliably in
manipulating LEGO bricks and the learning framework can effectively and safely
improve the manipulation performance to a 100% success rate. The co-design is
deployed to multiple robots (i.e. FANUC LR-mate 200id/7L and Yaskawa GP4) to
demonstrate its generalizability and transferability. In the end, we show that
the proposed solution enables sustainable robotic LEGO prototyping, in which
the robot can repeatedly assemble and disassemble different prototypes.

Robotics;Machine Learning;Neural and Evolutionary Computing