Deep Active Localization

Deep Active Localization

Active localization is the problem of generating robot actions that allow it to maximally disambiguate its pose within a reference map. Traditional approaches to this use an information-theoretic criterion for action selection and hand-crafted perceptual models. In this work we propose an end-to-end differentiable method for learning to take informative actions that is trainable entirely in simulation and then transferable to real robot hardware with zero refinement. The system is composed of two modules: a convolutional neural network for perception, and a deep reinforcement learned planning module. We introduce a multi-scale approach to the learned perceptual model since the accuracy needed to perform action selection with reinforcement learning is much less than the accuracy needed for robot control. We demonstrate that the resulting system outperforms using the traditional approach for either perception or planning. We also demonstrate our approaches robustness to different map configurations and other nuisance parameters through the use of domain randomization in training. The code is also compatible with the OpenAI gym framework, as well as the Gazebo simulator.

People

Sai Krishna G.V.

    Sai Krishna G.V.



Advisor: Liam Paull    
Thesis: Deep active localization
Current Position: Reinforcement learning researcher at AI-Redefined
Dhaivat Bhatt

    Dhaivat Bhatt



Advisor: Liam Paull    
Thesis: Variational aleatoric uncertainty calibration in neural regression
Current Position: Research engineer at Samsung
Krishna Murthy Jatavallabhula

    Krishna Murthy Jatavallabhula



Advisor: Liam Paull    
Current Position: PostDoc at MIT with Antonio Torralba and Joshua Tenenbaum
Vincent Mai

    Vincent Mai



Advisor: Liam Paull    
Current Position: AI researcher at the Institut de Recherche d'Hydro Qu├ębec (IREQ)
Liam Paull

    Liam Paull



Interests: Robot perception, uncertainty, sim2real, and robot benchmarking

Department of Computer Science and Operations Research | Université de Montréal | Mila