We invited guest speaker Viviane Clay from the University of Osnabrück to talk about her research on learning sparse and meaningful representations through embodiment. In the first part, she explores how these types of representations of the world are learned in an embodied setting by training a deep reinforcement learning agent on a 3D navigation task with RGB images as main sensory inputs. She then discusses how the model learns sparse encoding of high dimensional visual inputs without explicitly enforcing sparsity, and what the possible hypothesis for this phenomena are.
In the second part, she covers her undergoing work on extracting concepts by identifying a minimal set of co-occurring activations that represents an object in a curiosity-driven learning setting. These concepts can be used to improve sample efficiency and performance in downstream tasks, such as object classification or the full reinforcement learning task.