Hi, I'm Harsh Mahesheka

Electrical Engineering Senior at Indian Institute of Technology (BHU), Varanasi

The desire to build robust and adaptive autonomous systems that can work alongside humans to solve complex real-world tasks motivates me to study robotics. My research usually focuses more on Reinforcement Learning, Computer Vision and Robotics. Besides trying to make robots work, I enjoy clash of arguments in the form of debates.

harsh.mahesheka.eee20@iitbhu.ac.in

News

[Oct 2024] : Paper accepted at LangRob Workshop, Conference of Robot Learning (CoRL).
[Sept 2024] : Submitted a journal at Robotics Automation Letters (RAL).
[May 2024] : Started Internship at Hindustan Unilever.
[May 2024] : Got Selected for Google Summer of Code Program at Project Mesa
[May 2024] : Started Research Internship at Intelligent Robotics and Interactive Systems, ASU.
[Feb 2024] : Attended the Google Research Week, 2024.
[Aug 2023] : Presented my Houshold Bot Project at G20 Summit at Varanasi, India.
[May 2023] : Started Research Internship at Robot Learning Lab, University of Freiburg.
[Feb 2023] : Got nominated for DAAD WISE Scholarship and IUSSTF-Viterbi India Program.
[June 2022] : Appointed as Secretary of Robotics Club, IIT BHU.
[May 2022] : Started Research Internship at Robotics Research Center, IIIT Hyderabad.
[May 2022] : Got Selected for Google Summer of Code Program at Open Robotics.
[March 2021] : Stood first in Robotics Conclave and Scientist of Utopia at Technex (Annual Technical Fest of IIT BHU).
[Nov 2020] : Started Integrated BTech + Mtech in Electrical Engineering at IIT BHU

Publications

Zero-Cost Whole-Body Teleoperation for Mobile Manipulation

Daniel Honerkamp*, Harsh Mahesheka*, Jan Ole von Hartz, Tim Welschehold, Abhinav Valada
Sumbitted to IEEE Robotics Automation Letters (RA-L)

Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos

Harsh Mahesheka, Zhixian Xie, Zhaoran Wang, Wanxin Jin
Accepted at LangRob Workshop at Conference of Robot Learning (CoRL) 2024

Website
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Experiences

Intelligent Robotics and Interactive Systems Lab, Arizona State University

Research Internship Under Dr. Wanxin Jin and Dr. Zhaoran Wang
Developed a hierarchical VLM-LLM architecture for inverse reinforcement learning from videos by generating reward functions. The framework uses a high-level VLM (Gemini 1.5) to analyze expert and learner videos, providing feedback that a low-level LLM (GPT-4) uses to update the reward function.

Robot Learning Lab, University of Freiburg

DAAD-WISE Research Internship Under Dr. Abhinav Valada
Developed an RL-based planner to generate base velocities for mobile manipulators, enabling them to achieve end-effector goal poses provided by the teleoperator. Utilized the planner to streamline the collection of demonstrations for kinesthetic learning.

Google Summer of Code - Project Mesa

Student Developer at Project Mesa
Enhanced the Mesa agent-based modeling framework for Multi-Agent Reinforcement Learning by creating wrappers and functions for seamless integration with reinforcement learning libraries like RLlib and Stable Baselines. Created tutorials and documentation for getting started.

Google Summer of Code - Open Robotics

Student Developer at Open Robotics
Created a tool for generating template packages for the Gazebo simulator and macros for installing and sourcing worlds, models, plugins, etc., in the Gazebo robotics simulator. These tools were designed to streamline the process of creating and deploying new packages.

Hindustan Unilever

Tech Intern at Mumbai HO
Energy Optimizer: Used machine learning and data analytics to develop a tool to serve as an end-to-end solution for optimizing parameters to enhance factory energy efficiency, generating energy insights worth 3.5 crores.

Robotics Research Center, IIIT Hyderabad

Research Internship Under Dr. K. Madhava Krishna
Worked on an RL-based Global Planner trained to reduce localization drift by avoiding symmetric or featureless areas. Also worked on grounding waypoints on annotated maps by analyzing complex language commands in indoor environments.