 For that, we present an efficient vision-based controller that uses a deep autoencoder network  to compute a compact representation of the object's infinite dimensional shape. To deal with the (typically unknown) mechanical properties of the object, we use an online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features .
<coherence>  For that, we present an efficient vision-based controller that uses a deep autoencoder network  to compute a compact representation of the object's infinite dimensional shape. To deal with the (typically unknown) mechanical properties of the object, we use an online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features .
 For that, we present an efficient vision-based controller that uses a deep autoencoder network to compute a compact representation of the object's infinite-dimensional shape. An online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features .