Apoorva Vashisth

Hall of Data Science and AI

Purdue University

West Lafayette, IN 47907

Hi, I am Apoorva, a Ph.D. student in Computer Science Department at Purdue University where I work in the IDEAS Lab . My research focus lies at the intersection of embodied AI, vision-language-action models, agentic AI, long-horizon reasoning, and robot planning. My work focuses on developing learning-based methods that enable robots and multi-robot systems to reason, plan, and act effectively in complex environments.

Before joining Purdue, I earned a dual B.Tech. and M.Tech. in Mechanical Engineering from IIT Kharagpur and conducted research at the National University of Singapore and the University of Bonn. My work has been published in venues including ECCV, IEEE RAL, and CoRL.

Outside research, I love building and making things with my hands. I regularly spend time at Purdue’s Bechtel Innovation Design Center, experimenting with woodworking, metalworking, and forging in the hot works lab. Moving between code, machines, and physical materials keeps me curious and gives me a satisfying way to turn ideas into tangible objects.

news

Jun 18, 2026	Our paper titled “CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation” has been accepted at the European Conference on Computer Vision (ECCV 2026) 🎉
Dec 16, 2025	Our paper titled "Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning" has been accepted at the IEEE Robotics and Automation Letters (IEEE RA-L 2025) 🎉
Jun 14, 2024	First lead-author paper in journal! "Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning" has been accepted at the IEEE Robotics and Automation Letters (IEEE RA-L 2024) 🎉
Aug 30, 2023	Our paper titled "CAtNIPP: Context-Aware Attention-based Network for Informative Path Planning" has been accepted at the Conference on Robot Learning (CoRL-2023) 🎉
Sep 29, 2022	Featured in IITKGP Foundation (USA) Alumni Newsletter - September Edition .

selected publications

ECCV

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

Apoorva Vashisth, Manav Kulshrestha, Pranav Bakshi, and 3 more authors

European Conference on Computer Vision, 2026

Bib PDF Video Code Website

@article{vashisth2026move,
  title = {CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation},
  author = {Vashisth, Apoorva and Kulshrestha, Manav and Bakshi, Pranav and Conover, Damon and Sartoretti, Guillaume and Bera, Aniket},
  journal = {European Conference on Computer Vision},
  year = {2026},
}

RAL

Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning

Apoorva Vashisth, Manav Kulshrestha, Damon Conover, and 1 more author

IEEE Robotics and Automation Letters, 2026

Bib PDF Video Code Website

@article{vashisth2026scalable,
  title = {Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning},
  author = {Vashisth, Apoorva and Kulshrestha, Manav and Conover, Damon and Bera, Aniket},
  journal = {IEEE Robotics and Automation Letters},
  year = {2026},
  publisher = {IEEE},
}

RAL

Deep reinforcement learning with dynamic graphs for adaptive informative path planning

Apoorva Vashisth, Julius Rückin, Federico Magistri, and 2 more authors

IEEE Robotics and Automation Letters, 2024

Bib PDF Video Code Website

@article{vashisth2024deep,
  title = {Deep reinforcement learning with dynamic graphs for adaptive informative path planning},
  author = {Vashisth, Apoorva and R{\"u}ckin, Julius and Magistri, Federico and Stachniss, Cyrill and Popovi{\'c}, Marija},
  journal = {IEEE Robotics and Automation Letters},
  volume = {9},
  number = {9},
  pages = {7747--7754},
  year = {2024},
  publisher = {IEEE},
}