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Anirban Santara

Anirban Santara

Anirban Santara is a software engineer at Google Research India. Prior to this, he was a Google PhD Fellow at Indian Institute of Technology Kharagpur where he studied reinforcement learning algorithms for safe and efficient planning in autonomous driving. His current research interests include reinforcement learning for web-scale applications and representation learning for semantic robot navigation.

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    A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
    Sohan Rudra
    Saksham Goel
    Anirban Santara
    Claudio Gentile
    Laurent Perron
    Fei Xia
    Vikas Sindhwani
    Carolina Parada
    Gaurav Aggarwal
    NeurIPS 5th Robot Learning Workshop: Trustworthy Robotics (2022) (to appear)
    Preview abstract Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.). We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e.g. fruits, glasses, phones, etc.) that frequently change their positions due to human interaction. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability. View details
    Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback
    Anirban Santara
    Gaurav Aggarwal
    Shuai Li
    Claudio Gentile
    (2022) (to appear)
    Preview abstract Motivated by problems of ranking with partial information, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses. We formulate two generative models for this problem within the generalized linear setting, and design and analyze upper confidence algorithms for it. Our analysis delivers tight regret bounds which, when specialized to standard cascading bandits, results in sharper guarantees than previously available in the literature. We evaluate our algorithms against a representative sample of cascading bandit baselines on a number of real-world datasets and show significantly improved empirical performance. View details
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