Project: Graph Neural Networks for Robotic Grasping

Key information:

Abstract: 

Robotic systems provide the potential to support humans for physically strenuous tasks and in scenarios
which are unsafe, such as search-and-rescue. To operate in such environments a robot needs to control
how it physically interacts with its surroundings; the force it exerts on an object depends on the object’s
properties such as weight and brittleness. Robots are equipped with sensors to recognise objects and
infer physical properties. However, sensors or the object itself may be damaged, and robots cannot
rely on haptics in the same way as humans.

The aim of this project is to allow robots to recognise objects and navigate environments using Graph
Neural Networks (GNNs) and Few-shot learning. Learning from fewer examples is one of the big
challenges currently facing machine learning. Suitable datasets for the task are unavailable for training,
and GNNs are highly suited to Few-shot learning due to inferring missing labels from a graph which
encodes the similarity between datapoints.

Many well-known machine learning frameworks can be thought of as special cases of GNNs. Reducing
the computational cost of GNNs for real-world graphs is key for desirable embedded applications such
as robotics. The ubiquity of graph-structured data means that progress in graph learning can facilitate
breakthroughs across disciplines.