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Hola Amigo👋

I'm Ansh Shah, an enthusiast at the intersection of mechanical engineering, physics, and robotics. I recently graduated from BITS Pilani University (Pilani Campus) with degrees in BE Mechanical Engineering and MSc Physics in May 2023. Currently, I'm immersed in the world of research as a Research Intern at the Robotics Research Center under the guidance of Professor Madhava Krishna at IIIT Hyderabad.

My research pursuits span the realms of Deep Learning, Reinforcement Learning, and Multi-Agent SLAM, with a dash of passion for robotics hardware. My portfolio encompasses foundational projects in deep learning and computer vision. While I'm currently engrossed in mastering reinforcement learning, my excitement lies in bringing these concepts to life within real robotic systems. Notably, I've contributed to a Multi-Agent SLAM pipeline at RRC and am presently engrossed in crafting a loop closure detection module for the SLAM pipeline. Additionally, I'm actively involved in developing a differentiable local planner, further enhancing my expertise.

Here's a glimpse of my ongoing endeavours:

  • 🔭 Presently, I'm engrossed in a Multi-Agent SLAM project tailored for Quadcopters, which involves developing a Loop Closure Detection module utilizing cutting-edge DL techniques.
  • 🛤️ I'm also deeply engaged in building a differentiable local planner, a project that adds a new dimension to my expertise and understanding.
  • 🌱 My current learning trajectory involves diving deep into Multi-Agent SLAM with a focus on pose graph optimization and its practical implementation within a Drone Swarm.
  • 👯 I'm fervently open to collaboration on projects centred around aerial robotics, driven by a shared passion for technological innovation.
  • 🤔 I'm actively seeking guidance for potential avenues in Master's programs and job/research opportunities within the captivating realm of robotics.
  • 💬 I'm always up for discussions ranging from books, sports, and philosophy to psychology, art, chess, movies, guitar, technology, and environmental studies.
  • ⚡ Fun fact: "Nothing is original. Steal like an Artist."

Feel free to connect with me via email at [email protected] or [email protected]. I'm always eager to engage in meaningful conversations.

Selected Highlights from My Project Portfolio:

  • Multi-Agent SLAM Pipeline (VINS-MONO-GTSAM)
  • Micromouse
  • Autonomous Quadcopter
  • Quadruped Robot
  • Monocopter
  • Participation in the International Robotics Challenge at Techfest, IIT Bombay in 2019
  • Contribution to the Quadcopter for GameOfDrones at Techfest, IIT Bombay in 2019
  • Cronus (an innovative surveillance bot concept)
  • Differentiable Local Planner (Current Project)

Looking forward to exciting possibilities and collaborations in the dynamic world of robotics.

Ansh Shah's Projects

advice icon advice

A repository of links with advice related to grad school applications, research, phd etc

automated_drone icon automated_drone

Automation of a quadcopter with kinect and sensors to acheive Amazon Statement

bottle-ml-dataset icon bottle-ml-dataset

Ml-Datasets so that it can be loaded from any computer and no hassle to make folder on your computer.

ellipsoid-fit icon ellipsoid-fit

Approximate a cloud of points (3D/2D) with the inner and outer ellipsoid. Also known as the inner and outer Löwner-John ellipses.

gameofdrones icon gameofdrones

Drone can carry a small payload , most probably a medical box and can hav QR scanning capablity for Information Transfer

info icon info

Github Pages template for academic portfolio websites

micromouse icon micromouse

We made a Micromouse (maze solver) for IIT Bombay Techfest Competitions in 2018 and 2019.

noisy_odom icon noisy_odom

This package adds a node to add drift to Odometry_msgs from simulation to create loop closure dataset

quadruped icon quadruped

Project to learn high level concepts of Robotics and embedded systems

vins-mono-gtsam icon vins-mono-gtsam

A Robust and Versatile Monocular Visual-Inertial State Estimator. Adding GTSAM implementation in backend

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