My Portfolio

Hi, I am Siddharth. I am a Ph.D. student in the Mechanical & Aerospace Engineering Department at the University of Virginia currently advised by Prof. Cindy Chang. My research focuses on robotics and learning. The emphasis of my research is to make robots more reliable and safer to operate in industrial environments.

Prior to joining UVA, I completed my M.S.E. from the University of Pennsylvania in Mechanical Engineering and Applied Mechanics (2018). I earned my B.E. in Manufacturing Process & Automation Engineering from NSIT, New Delhi, India (2014). I enjoy playing badminton, cricket and anything related to cars.

Research

I have worked in numerous aspects of robotics and my expertize lies on applied robotics. The aim of my research is to reduce the cost of reprogramming robots and improve their online behaviour with little human supervision.

I enjoy designing and building things. From CAD to fabbrication to mechanism design to electronic system development to control to learning based motion planning, through my time in the different organizations and institutions I have worked on developing a wide variety of skillset.

Latest News

[Feb 2025] Our paper on multi-arm motion planning has been selected to be presented at ICRA 2025!!

[Feb 2025] Our paper on improvised DMP with RL-LfD has been accepted for publication to RCIM, Elsevier!!

[Nov 2024] I was inducted into the Raven Society, one of the oldest and most prestigious societies at UVA.

[Sep 2023] I have joined Prof. Cindy Chang's Intelligent Systems Lab.

[Sep 2023] I have been awarded the SEAS Teching Fellowship to co-teach Analytical Dynamics (MAE 6320).

[May 2023] I will be joining CCC Intelligent Solutions as a Data Science R&D Intern.

[Mar 2023] I won the Link Lab CCI Interdisciplinary Research Proposal Competition ($2000).

[Aug 2022] I presented our work at ASME IDETC Conference in St. Louis, Missouri.

[Aug 2022] I have passed my Qualifier Exams!

[Jan 2021] I started my Ph.D. in Mechanical & Aerospace Engineering Department at the University of Virginia

Publications

Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing Pre-print
Siddharth Singh*, Tian Yu*, Qing Chang, John Karigiannis, Shaopeng Liu
*Equal Contribution, arXiv Pre-print https://doi.org/10.48550/arXiv.2502.19340

Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives Conference
Siddharth Singh, Tian Xu, Qing Chang
ICRA 2025, IEEE, Pre-print https://doi.org/10.48550/arXiv.2410.00757

Generalizing kinematic skill learning to energy efficient dynamic motion planning using optimized Dynamic Movement Primitives Journal
Tian Xu*, Siddharth Singh*, Qing Chang
*Equal Contribution, Robotics and Computer Integrated Manufacturing, Elsevier, https://doi.org/10.1016/j.rcim.2025.102983

Hierarchical Learning for Robotic Assembly Leveraging LfD Journal
Siddharth Singh, Qing Chang, Tian Yu
arXiv Pre-print, Under Review

Lie Theory Based Optimization for Unified State Planning of Mobile Manipulators Pre-print
William Smith, Siddharth Singh, Julia Rudy, Yuxiang Guan
arXiv Pre-print, https://doi.org/10.48550/arXiv.2410.15443

A Multistage Framework for Autonomous Robotic Mapping with Targeted Metrics Journal
William Smith, Yongming Qin, Siddharth Singh, Hudson Burke, Tomonari Furukawa, Gamini Dissanayake
Robotics. 2023; 12(2):39. https://doi.org/10.3390/robotics12020039

Design of a photometric stereo-based depth camera for robotic 3D reconstruction Conference
Kallia Smith, Hannah Lothrop, Siddharth Singh, Tomonari Furukawa
Proc. SPIE 12720, 2022 Workshop on Electronics Communication Engineering, 127200M (28 June 2023); https://doi.org/10.1117/12.2675449

Photometric Stereo Enhanced Light Sectioning Measurement for Microtexture Road Profiling Conference
Siddharth Singh, Kallia Smith, Tomonari Furukawa
Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. St. Louis, Missouri, USA. August 14–17, 2022. V007T07A056. ASME. https://doi.org/10.1115/DETC2022-91154