text: 489,4,1036617608160862208,2427297348,Ashish Mehta,"Our new paper ""Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision"", outlines a Multi-Task Learning from Demonstration framework for end-to-end autonomous driving in urban environments. With @AdithyaSub86 and Anbumani Subramanian. <LINK>",https://arxiv.org/abs/1808.10393,"Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator's driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity. ","Learning End-to-end Autonomous Driving using Guided Auxiliary