Satrajit Chatterjee

Hi there! 👋

I am a first-year Master of Science in Engineering (MSE) in robotics at the University of Pennsylvania.

My motivation towards a career in robotics began with a philosophical question — how do we emulate consciousness? The abilities that allow us humans to generalize skills and adapt to un- certainty have captivated and eluded generations of engineers, scientists and thinkers. I want to help realize a significant breakthrough that would enable autonomous systems to convincingly emulate human behavior and introduce a paradigm shift in society.

Previously I have engaged in research at the Jet Propulsion Laboratory in NASA and at the AirLab in Carnegie Mellon University. Our work on tree-based informative path planning (TIGRIS) was published in IROS’22. At AWS I worked on DeepRacer - an autonomous 1/18th scale race car designed to test RL models by racing on a physical race track.

I am also an adventure junkie who loves exploring places off-the-beaten path. I am a PADI Open Water certified diver 🐠, amateur rock-climber 🧗‍♂️ and working on my Private Pilot’s License (PPL) 🛩️.

publications

2022

TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning

Brady Moon, Satrajit Chatterjee, and Sebastian Scherer

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022

Abs Video Bib PDF Website

Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces.
@inproceedings{moon2022tigris, doi = {10.1109/IROS47612.2022.9981992}, author = {Moon, Brady and Chatterjee, Satrajit and Scherer, Sebastian}, title = {TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2022}, video = {https://youtu.be/bMw5nUGL5GQ}, }