Karmesh Yadav

Karmesh Yadav

AI Resident at FAIR

FAIR

Biography

Hi! I am Karmesh, an AI Resident at FAIR, working with the Habitat team under the supervision of Dr. Oleksandr Maksymets and Prof. Dhruv Batra.

I’m interested in robot learning, and have been researching how can we learn better visual representations for downstream training of Embodied AI agents. Previously, I worked as a Senior Robotics Engineer at ISEE, an autonomous vehicles startup based out of Boston, trying to automate the yard trucking space. I completed my Masters in Robotics Systems Development (MRSD) at the CMU Robotics Institute in 2020.

Download my resumé.

Interests
  • Reinforcement Learning
  • Robot Learning
  • Meta Learning
Education
  • Masters in Robotic Systems Development, 2020

    Carnegie Mellon University

  • B.Tech in Mechanical Engineering, 2017

    Indian Institute of Technology, Guwahati

Experience

 
 
 
 
 
AI Resident
Aug 2021 – Present Menlo Park
  • Researching self-supervised pretraining techniques for learning useful representations for embodied agents.
  • Using the learnt representations on downstream RL tasks like image-goal navigation and object-goal navigation in the Habitat Simulator.
 
 
 
 
 
Robotics Engineer
Jul 2020 – Aug 2021 Pittsburgh
  • Explored deep uncertainty estimation techniques for predicting the closed loop tracking performance of an autonomous vehicle controller. Estimated the collision probability of the AV with respect to obstacles in an occupancy grid.
  • Improved the trajectory optimization planner and robustified its collision checking. This led to an increased confidence in its performance and resulted in its deployment on the AV.
  • Developed the speed planning module for safely achieving three-fold increase in the operating speed of the AV.
 
 
 
 
 
Software Development Intern
May 2019 – Aug 2019 Boston
  • Built toolboxes to automate the system identification and calibration procedure of Isee’s vehicles.
  • Researched and implemented various vehicle and tire models for control application in AVs.
 
 
 
 
 
Graduate Research Assistant
Aug 2017 – Jun 2018 Hyderabad
  • Created a Q-Learning based planner to prevent monocular slam failure on non-holonomic robots.
  • Developed a simulation environment in Gym-Gazebo for training, with Navigation Stack for planning and ORB-SLAM for perception and localization.
 
 
 
 
 
Intern, Autonomous Driving Team
Aug 2017 – Nov 2017 Hyderabad
  • Optimized ORB-SLAM and made it more robust to fuse its position output with RTK-GPS, IMU & Wheel Encoder data using an EKF.
  • Worked on SLAM pose covariance estimation and extrinsic calibration of IMU and cameras
 
 
 
 
 
Research Intern
May 2016 – Jul 2016 Taipei
  • Developed a two-level motion planner, utilizing the A-star (A*) and Rapidly-exploring Random Tree (RRT) algorithm, on a local map built using laser scanners for an electric golf cart.
  • Created a vehicle model and forward-simulated the vehicle trajectory using Pure Pursuit steering controller and Proportional-Integral (PI) speed controller.

Publications & Preprint

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Projects

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Soft Robotic Hand

Soft Robotic Hand

Learning Dexterous Manipulation with a Low-Cost Soft Robotic Manipulator

CiLQR for Motion Planning

CiLQR for Motion Planning

Constrained iLQR for Motion Planning in Autonomous Vehicles

Collision Avoidance System

Collision Avoidance System

Detection and Response for Potential Head-On Vehicle Crashes

Autonomous Driving using IRL

Autonomous Driving using IRL

Learning from Demonstrations with Inverse Reinforcement in CARLA

Very Social Robot

Very Social Robot

Learning Human-Robot Tool Handover using Interaction

Formula Bharat

Formula Bharat

A formula-styled race car developed in IIT Guwahati

Recent & Upcoming Talks

NeurIPS 2020 Oral Presentation
An Oral Presentation on Look-Ahead MAML
NeurIPS 2020 Oral Presentation

Contact