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Raunaq Bhirangi

Raunaq Bhirangi

Postdoctoral Researcher in Robotics & AI at NYU

raunaq-bhirangi
New York, New York
Joined May 2026
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Summary

Raunaq Bhirangi is a 'full-stack roboticist' with expertise spanning hardware development, real-world robot learning, and dexterity. His work aims to bring robots into unstructured, everyday environments by enabling them to perform precise, contact-rich tasks. raunaqb+1
He is a leading researcher in tactile sensing for robotics, developing innovative solutions like eFlesh, ReSkin, and AnySkin. These projects focus on creating versatile, 3D-printable, and plug-and-play tactile sensors to improve robot perception and interaction. raunaqb+5
His research integrates vision and touch for highly precise robot manipulation, as demonstrated by his ViTaL (VisuoTactile Local policies) framework. This approach allows robots to learn complex tasks, such as plugging USBs or swiping cards, with high success rates from minimal real-world interaction. nitter+6
Raunaq Bhirangi has made contributions to the open-source robotics community, including his involvement in the release of the RUKA Hand, a low-cost robotic hand, as an accessible and affordable kit. nitter+4
His academic background includes a PhD and MS in Robotics from Carnegie Mellon University, following a Bachelor's in Mechanical Engineering from IIT Bombay, demonstrating a strong foundation in both mechanical design and advanced robotics research. raunaqb+3

Work

Education

Projects

Writing

Anyskin: Plug-and-play skin sensing for robotic touch

January 1, 2025

A paper introducing the AnySkin system, which provides an easy-to-integrate tactile sensing solution for robots.

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eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures

January 1, 2025

Details the eFlesh tactile sensor, highlighting its customizability and magnetic sensing technology for robotic applications.

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Touch begins where vision ends: Generalizable policies for contact-rich manipulation

January 1, 2025

This paper presents a framework for creating generalizable robot policies for manipulation tasks that require significant contact, emphasizing the complementary roles of vision and touch.

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Hierarchical state space models for continuous sequence-to-sequence modeling

January 1, 2024

A work exploring hierarchical state space models for handling continuous sequential data, relevant to robot control and learning.

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Learning precise, contact-rich manipulation through uncalibrated tactile skins

January 1, 2024

A research paper on enabling precise, contact-rich robot manipulation using uncalibrated tactile sensors.

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All the feels: A dexterous hand with large-area tactile sensing

January 1, 2023

A publication describing the development of a dexterous robotic hand equipped with extensive tactile sensing capabilities.

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Reskin: versatile, replaceable, lasting tactile skins

January 1, 2021

A research paper presenting a solution for robust and adaptable tactile sensing for robots.

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Modular robot design synthesis with deep reinforcement learning

January 1, 2020

Research on using deep reinforcement learning for the design of modular robots.

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Hobbies

Enjoys hiking, reading, cooking, and photography. raunaqb

Engages in discussions about history, culture, politics, and philosophy. raunaqb