Rahul Mitra

I am a computer science PhD student at Boston University advised by Prof. Edward Chien. Our research is broadly in the areas of computer graphics and geometry processing.

Previously, I received my bachelor's degrees in Computer Science and Physics from Trinity College. My undergraduate research, advised by Prof. Kevin Huang, was broadly in the area of telerobotics. My teaching involved assistance in Computer Science, Physics and Engineering courses.

My graduate coursework includes:

  • Randomized Algorithms
  • Graduate Computer Graphics
  • Advanced Optimization Algorithms
  • Geometry Processing

Email  /  CV  /  Resume  /  Google Scholar  /  Github

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Research

During undergrad, my research largely involved projects on teleoperation, evaluating haptic interfaces and contact sensing. Check out our work below!

Recently, I've been working on applying geometry processing techniques to the realm of digital fabrication, with a focus on computational knitting.

PontTuset Telelocomotion-Remotely Operated Legged Robots
Kevin Huang, Divas Subedi, Rahul Mitra, Isabella Yung, Kirkland Boyd, Edwin Aldrich, Digesh Chitrakar
MDPI Applied Sciences, 2021
paper

This work introduces the idea of extending teleoperation to enable online human remote control of legged robots, or telelocomotion, to traverse challenging terrain. A haptic telelocomotion interface was developed. Two within-user studies validate the proof-of-concept interface and our results are promising to the use of haptic feedback for telelocomotion in complex traversal tasks. This work builds on our 2020 IRC poster paper.

PontTuset Contact Sensing via Active Oscillatory Actuation
Rahul Mitra, Kirkland Boyd, Divas Subedi, Digesh Chitrakar, Edwin Aldrich, Ananya Swamy, Kevin Huang
3rd International Conference on Mechatronics, Robotics and Automation (ICMRA), 2020
paper

In this work, a contact sensor that is minimally intrusive and can be subsumed into extant devices is prototyped and tested. Oscillatory acceleration data is collected and subsequently used to train and classify different contact locations using frequency-based features. Three separate classes are distinguished according to contact location. Results are promising and show excellent classification of both contact and contact location.

PontTuset Characterizing limits of vision-based force feedback in simulated surgical tool-tissue interaction
Kevin Huang; Digesh Chitrakar; Rahul Mitra; Divas Subedi; Yun-Hsuan Su
42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020
paper

This work attempts to empirically evaluate the degree to which haptic feedback may deviate from ground truth yet result in acceptable teleoperated performance in a simulated RMIS-based palpation task. A preliminary user-study is conducted to verify the utility of the simulation platform, and the results of this work have implications in haptic feedback for RMIS and inform guidelines for vision-based tool-tissue force estimation.

PontTuset Haptic Interface for Hexapod Gait Execution
Digesh Chitrakar, Rahul Mitra, Kevin Huang
4th IEEE International Conference on Robotic Computing (IRC), 2020
paper

This paper presents a method for leveraging human decision making and adaptability to control legged robot walking with a haptic interface. The magnitude and direction of force feedback as well as average step size were tracked during basic locomotion.

PontTuset Sampling of 3dof robot manipulator joint-limits for haptic feedback
Kevin Huang, Yun-Hsuan Su, Mahmoud Khalil, Daniel Melesse, Rahul Mitra
IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), 2019
paper / Oral Presentation / Slides.

In teleoperated robots, the kinematics and workspace of the remote device is oftentimes dissimilar to the input device, leading to potential confusion and frustration of the human operator. One solution is to constrain the input device motion to a scaled version of remote device joint ranges. This paper presents a method for doing so with 3 degree of freedom (DOF) manipulators and input devices with kinematic dissimilarities. The approach utilizes a simple tree structure, whereby a local Cartesian workspace limit is sampled and indexed by joint.

Service

  • Teaching Assistant: Data Structures & Algorithms (Spring '20, Spring '21), Mechanics (Fall '20), Introduction to Engineering Design: Mobile Robots (Spring '19) , Introduction to Computing (Spring '19).
  • Organizations: Trinity College Chapters of IEEE, RAS, ACM.


Template from Jon Barron's webpage.