Many applications of unpaired image-to-image translation require the input contents to be preserved semantically during translations. Unaware of the inherently unmatched semantics distributions between source and target domains, existing distribution matching methods (i.e., GAN-based) can give undesired solutions. In particular, although producing visually reasonable outputs, the learned models usually flip the semantics of the inputs. To tackle this without using extra supervisions, we propose to enforce the translated outputs to be semantically invariant w.r.t. small perceptual variations of the inputs, a property we call "semantic robustness". By optimizing a robustness loss w.r.t. multi-scale feature space perturbations of the inputs, our method effectively reduces semantics flipping and produces translations that outperform existing methods both quantitatively and qualitatively.

We outline a neural network-based pipeline for perception, control and planning of a 7 DoF robot for tasks that involve reaching into a dormant grapevine canopy. The proposed system consists of a 6 DoF industrial robot arm and a linear slider that can actuate on an entire grape vine. Our approach uses Convolutional Neural Networks to detect buds in dormant grape vines and a Reinforcement Learning based control strategy to reach desired cut-point locations for pruning tasks. Within this framework, three methodologies are developed and compared to reach the desired locations: the learned policy-based approach (RL), a hybrid method that uses the learned policy and an inverse kinematics solver (RL+IK), and lastly a classical approach commonly used in robotics. We first tested and validated the suitability of the proposed learning methodology in a simulated environment that resembled laboratory conditions. A reaching accuracy of up to 61.90% and 85.71% for the RL and RL+IK approaches respectively was obtained for a vine that the agent observed while learning. When testing in a new vine, the accuracy was up to 66.66% and 76.19% for RL and RL+IK, respectively. The same methods were then deployed on a real system in an end to end procedure: autonomously scan the vine using a vision system, create its model and finally use the learned policy to reach cutting points. The reaching accuracy obtained in these tests was 73.08%.