Ramanan Sekar

I am a Robotics Master's student at the GRASP Lab at the University of Pennsylvania. I am advised by Kostas Daniilidis, and work with his research group. I am broadly interested in problems at the intersection of Deep Learning and Robotics, and problems in Reinforcement Learning.

I received my Bachelor's degree in Electrical Engineering at Anna University in India, with my Senior Project advised by Ranganath Muthu. I have spent time as a Research Intern with P.V.Manivannan at IIT Madras, and as a Research Fellow with Urbasi Sinha at Raman Research Institute. I have also spent time at Qualcomm R&D working on using Deep Learning for error-correction codes.

| CV | GitHub | Twitter | LinkedIn | Email |

  Research
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Planning to Explore via Self-Supervised World Models
Ramanan Sekar*, Oleh Rybkin*, Kostas Daniilidis,
Pieter Abbeel, Danijar Hafner, Deepak Pathak

ICML 2020
Also presented at BeTR-RL Workshop at ICLR 2020.

webpage | code | pdf | arXiv | video |

In the Media: | VentureBeat | Synced | Reddit | Paper Review |

We propose a model-based agent for self-supervised reinforcement learning. Our agent is able to adapt in a zero/few-shot setup, achieving comparable performance to supervised state-of-the-art RL.

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Perception-Driven Curiosity with Bayesian Surprise
Beradette Bucher, Anton Arapin, Ramanan Sekar,
Feifei Duan, Marc Badger, Kostas Daniilidis, Oleh Rybkin

Workshop on Combining Learning and Reasoning at RSS, 2019

workshop page | pdf |

We learn a latent variable model for dynamics of image observations, and use it to construct an agent that maximizes Bayesian surprise of the future frames. The Bayesian agent can perform exploration that is more robust in stochastic environments than simpler prior prediction schemes.

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Modified Extended Kalman Filter Using Correlations Between Measurement Parameters
Ramanan Sekar, Sai Shankar N, Shiva Shankar B, P.V.Manivannan

International Conference on Computational Intelligence, 2018

conference | pdf | code |

We mathematically analyze the correlations that arise between measurement parameters. This is done by understanding the geometrical transformations that a data point undergoes when correlations are determined between normally distributed measurement parameters. We use this understanding to develop a new algorithm for the discrete Kalman Filter

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Use of measurement noise correlations for an improved SONAR model
Ramanan Sekar, Sai Shankar N, Shiva Shankar B, P.V.Manivannan

IEEE TAP Energy, 2017

conference | pdf |

We propose a solution to reduce the range and bearing error significantly, and thus improve the performance of the SONAR. Using the results from the Gaussian Correlation Inequality, we derive probabilistic transformations that can improve the range and bearing measurement of the SONAR, thus reducing the sensor error.

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A Report on Decoy State Quantum Key Distribution
Ramanan Sekar
Advised by Urbasi Sinha

Submitted Report for IAS SRFP 2017.

pdf |

QKD, a sub-topic of quantum cryptography, stands strong in its security only bounded by the laws of physics, meaning no matter how ad- vanced the technology gets, what is prohibited will be prohibited. This report outlines the fundamentals of the BB84 scheme and its problems, how the decoy state idea proved to be a worthy solution, along with some mathematical background and two important experimental milestones

  Selected Projects
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Master's Thesis
Ramanan Sekar
Advised by Kostas Daniilidis

Based on the paper Planning to Explore via Self-Supervised World Models

pdf | Thesis Talk |

This work focuses on task-agnostic exploration, where an agent explores a visual environment without yet knowing the tasks it will later be asked to solve.

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Senior Project: Collaboration between Unmanned Aerial and Ground Vehicles for Search and Rescue Missions
Ramanan Sekar, Sai Shankar N, Shiva Shankar B
Advised by Ranganath Muthu.

pdf | presentation |

This project work is an implementation of a collaborative robot system that help overcome the challenges in disaster rescue, where there is a focus on the application of autonomously searching and rescuing people in disaster zones such as earthquakes with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments


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