Anagha Dangle

Robotics Researcher & Engineer

I'm a first-year Ph.D. student at Johns Hopkins University, where I work with Prof. Krishna Murthy Jatavallabhula. I'm broadly interested in dexterous manipulation, with my current focus on leveraging tactile sensing and other active sensing mechanisms for contact-rich manipulation.

Before Hopkins, I was a Robotics Engineer at Amazon Robotics, working on multimodal sensing for robots in unstructured environments. I received my M.S. in Robotics Engineering from Worcester Polytechnic Institute in May 2024, where I worked on context-aware dexterous manipulation, advised by Prof. Berk Calli. My thesis was supported by the Amazon Robotics Greater Boston Tech Initiative grant.

I'd love to connect if you're interested in collaborating, or just want to talk research - feel free to reach out!

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Anagha Dangle

News

Experience

Johns Hopkins University
Sep 2026 – Present
Ph.D. Student, Computer Science (Robotics)
Amazon Robotics
Software Development Engineer II
Apr 2026 – Aug 2026
Software Development Engineer (R&D)
May 2024 – Apr 2026
Software Development Engineer (R&D) Intern
May 2023 – Aug 2023
Berkshire Grey
Jan 2024 – Apr 2024
Software Development Research Co-op
MER Lab, WPI
Aug 2022 – Dec 2023
Graduate Research Assistant
Advisor: Dr. Berk Calli
Hewlett Packard Enterprise
Mar 2022 – Jul 2022
Intern
Human-Centered Robotics Lab, IIT Gandhinagar
Jun 2021 – Aug 2021
Research Intern
Advisor: Dr. Vineet Vashishta
OmniPresent Robotech
Oct 2020 – Jan 2021
Intern
Mentors: Aakash Sinha

Publications

Leveraging Dexterous Picking Skills for Complex Multi-Object Scenes
A. R. Dangle, M. P. Deshmukh, D. Boby, and B. Calli. IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2024
TL;DRDeep learning framework that leverages four human-inspired dexterous picking skills for robust robotic manipulation in cluttered, multi-object scenes, validated through real-world grasping experiments.
Paper
Enhanced Colorization of Thermal Images for Pedestrian Detection using Deep Convolutional Neural Networks
Anagha Dangle, R. Mundada, S. Gore, J. Shrungare, and H. Dalal. International Conference on Machine Learning and Data Engineering (Springer), 2022
TL;DRLearns a thermal-to-RGB colorization model and shows that colorized inputs improve downstream pedestrian detection accuracy compared to grayscale thermal baselines.
Paper

Projects

ROBOCON 2021

Engineered a high-agility swerve-drive robot with a flywheel-based projectile mechanism and pneumatic autoloader for ball throwing task.

Robotics · Design · Controls
Design Doc

ROBOCON 2022

Developed an omni-drive mobile manipulator with a dual slider-crank parallel gripper for autonomous object detection and precision stacking.

Automation · Gripper Design · C++
Design Doc

ROBOCON 2020

Implemented a synchronized ball transfer system using a roller-flywheel mechanism for robust, high-speed handoffs between mobile robots.

System Integration · ROS · Simulation
Design Doc

Blog

Blog Post Title

Jan 15, 2024

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Photography