Search Results for author: Shurjo Banerjee

Found 7 papers, 1 papers with code

Iterative Vision-and-Language Navigation

no code implementations CVPR 2023 Jacob Krantz, Shurjo Banerjee, Wang Zhu, Jason Corso, Peter Anderson, Stefan Lee, Jesse Thomason

We present Iterative Vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time.

Instruction Following Vision and Language Navigation

The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation

no code implementations23 Oct 2020 Shurjo Banerjee, Jesse Thomason, Jason J. Corso

In each trial, the pair first cooperates to localize the robot on a global map visible to the Commander, then the Driver follows Commander instructions to move the robot to a sequence of target objects.

Navigate Simultaneous Localization and Mapping

A Geometric Approach to Online Streaming Feature Selection

no code implementations2 Oct 2019 Salimeh Yasaei Sekeh, Madan Ravi Ganesh, Shurjo Banerjee, Jason J. Corso, Alfred O. Hero

In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introduce a new setting where features and samples are streamed concurrently called OSFS with Streaming Samples (OSFS-SS).

feature selection

A Critical Investigation of Deep Reinforcement Learning for Navigation

1 code implementation7 Feb 2018 Vikas Dhiman, Shurjo Banerjee, Brent Griffin, Jeffrey M. Siskind, Jason J. Corso

However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps.

Navigate reinforcement-learning +1

Do Deep Reinforcement Learning Algorithms really Learn to Navigate?

no code implementations ICLR 2018 Shurjo Banerjee, Vikas Dhiman, Brent Griffin, Jason J. Corso

As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments.

Navigate reinforcement-learning +1

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