  The process of industrial box-packing, which involves the accurate placement
of multiple objects, requires high-accuracy positioning and sequential actions.
When a robot is tasked with placing an object at a specific location with high
accuracy, it is important not only to have information about the location of
the object to be placed, but also the posture of the object grasped by the
robotic hand. Often, industrial box-packing requires the sequential placement
of identically shaped objects into a single box. The robot's action should be
determined by the same learned model. In factories, new kinds of products often
appear and there is a need for a model that can easily adapt to them.
Therefore, it should be easy to collect data to train the model. In this study,
we designed a robotic system to automate real-world industrial tasks, employing
a vision-based learning control model. We propose in-hand-view-sensitive
Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to
obtain in-hand postures of objects. We demonstrate that our model, trained for
a single object-placement task, can handle sequential tasks without additional
training. To evaluate efficacy of the proposed model, we employed a real robot
to perform sequential industrial box-packing of multiple objects. Results
showed that the proposed model achieved a 100% success rate in industrial
box-packing tasks, thereby outperforming the state-of-the-art and conventional
approaches, underscoring its superior effectiveness and potential in industrial
tasks.

Robotics