no code implementations • 19 Dec 2018 • Yilun Chen, Praveen Palanisamy, Priyantha Mudalige, Katharina Muelling, John M. Dolan
In this paper, we leverage auxiliary information aside from raw images and design a novel network structure, called Auxiliary Task Network (ATN), to help boost the driving performance while maintaining the advantage of minimal training data and an End-to-End training method.
1 code implementation • 20 Nov 2018 • Arpit Agarwal, Katharina Muelling, Katerina Fragkiadaki
We propose an exploration method that incorporates look-ahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies .
no code implementations • 20 Jun 2018 • Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani
We introduce a novel deterministic policy gradient update, DRAG (i. e., DeteRministically AGgrevate) in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural parser.
2 code implementations • 12 Oct 2017 • Anirudh Vemula, Katharina Muelling, Jean Oh
In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity.
1 code implementation • 22 May 2016 • Anirudh Vemula, Katharina Muelling, Jean Oh
In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model.
Robotics