Search Results for author: Katharina Muelling

Found 5 papers, 3 papers with code

Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks

no code implementations19 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.

Optical Flow Estimation Semantic Segmentation +2

Model Learning for Look-ahead Exploration in Continuous Control

1 code implementation20 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 .

Continuous Control Reinforcement Learning (RL)

Learning Neural Parsers with Deterministic Differentiable Imitation Learning

no code implementations20 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.

Imitation Learning

Social Attention: Modeling Attention in Human Crowds

2 code implementations12 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.

Navigate Trajectory Prediction

Path Planning in Dynamic Environments with Adaptive Dimensionality

1 code implementation22 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.


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