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 in the area of telerobotics.

I'm excited to be TA-ing Ed's Geometry Processing course this semester! (Spring, 23')

My graduate coursework includes:

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

Email  /  Resume  /  Google Scholar  /  Github

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

More recently, I've been working on applying geometry processing techniques to the realm of digital fabrication. Our work on Helix-Free Stripes was accepted at SIGGRAPH, 23'! See you in LA.

The problem of placing evenly-spaced stripes on a triangular mesh mirrors that of having evenly-spaced course rows and wale columns in a knit graph for a given geometry. This work presents strategies for producing helix-free stripe patterns and traces them to produce helix-free knit graphs suitable for machine knitting. We optimize directly for the discrete differential (1-form) of the stripe texture function, i.e., the spinning form, and demonstrate the knitting-specific advantages of this framework. In particular, we note how simple linear constraints allow us to place stitch irregularities, align course rows and wale columns to boundary/feature curves, and eliminate helical stripes. Two mixed-integer optimization strategies using these constraints are presented and applied to several mesh models. The results are smooth, globally-informed, helix-free stripe patterns that we trace to produce machine-knittable graphs. We further provide an explicit characterization of helical stripes and a theoretical analysis of their elimination constraints.

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.

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.

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.

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.

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.

• Teaching Assistant: Geometry Processing (Spring '23), 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.