PyTorch

Submission is in progress

Submission is in progress

Humans posess a vast sensorimotor knowledge base that must be utilized to accelerate robot policy learning. While existing methods of demonstration generally ask humans to directly manipulate the robot’s state, this work utilizes a more natural demonstration capture platform (i.e. mouse and keyboard) to train deep learning networks on a motion planning task. Through qualitative and quantitative analysis, we demonstrate that such methods can successfully supervise imitation learning policies.

By learning the common structure between all LDR-to-HDR conversion tasks, our model is able to adapt it’s predictions given extra exposures of a scene. This novel approach reframes LDR-to-HDR conversion as a meta-learning problem.

This final project explores the feasibility of using a GAN based style-transfer approach to generating semantic segmentation masks for autonomous driving application. Specifically, CycleGAN and pix2pix are benchmarked against a more traditional U-Net architecture built off of Resnet-18. The project uses a subset of the image data from the ApolloScape open dataset.