I am an Applied Scientist at Amazon Robotics. I recently wrapped up my Ph.D. (ABD) in Computer Science from Rutgers University. I was part of the PRACSYS lab, advised by Kostas Bekris. I am broadly interested in applying machine learning algorithms to improve the efficiency and robustness of robot planning. I am excited about building robust, real-world robotics systems that integrate planning, control and learning.
Prior to my Ph.D., I earned an MS in CS from Rutgers. I earned my undergraduate degree in Computer Science & Engineering from Amrita Vishwa Vidyapeetham University. I have been fortunate to intern at some great places:
- Amazon Robotics: working with Marcelo Kallmann (Summer 2022)
- Bosch Austin: working with Alessandro Allievi, Jarrett Holtz and Xuesu Xiao (Summer 2021)
- Preferred Networks: working with Wilson Ko and Kuniyuki Takahashi (Summer 2018)
E-mail: aravind.siva@rutgers.edu
My Rutgers collaborators and I are always looking for talented and motivated undergraduate / M.S. student collaborators. If you are interested in my research, please e-mail me describing your background and interests.
This page is perpetually under construction until I say otherwise. You can find me on Github and Twitter. My CV can be found here.
[Papers] [Teaching] [Projects] [Misc]Preprints
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Coming soon...
Conference and Journal Papers
- Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Noah R. Carver, Kostas E. Bekris. Roadmaps with Gaps over Controllers: Achieving Efficiency in Planning under Dynamics. Proceedings of the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024. [arXiv]
- Ewerton R. Vieira*, Aravind Sivaramakrishnan*, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, Kostas E. Bekris. MORALS: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024. Best Paper Award in Automation finalist. [arXiv]
- Ewerton R. Vieira, Aravind Sivaramakrishnan, Yao Song, Edgar Granados, Marcio Gameiro, Konstantin Mischaikow, Ying Hung, Kostas E. Bekris. Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [pdf][arXiv]
- Troy McMahon*, Aravind Sivaramakrishnan*, Edgar Granados and Kostas E. Bekris, A Survey on the Integration of Machine Learning with Sampling-based Motion Planning. Foundations and Trends® in Robotics: Vol. 9: No. 4, pp 266-327. [pdf] [arXiv] [webpage]
- Troy McMahon, Aravind Sivaramakrishnan, Kushal Kedia, Edgar Granados, Kostas E. Bekris, Terrain-Aware Learned Controllers for Kinodynamic Planning over Physically Simulated Terrains. Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. [pdf]
- Ewerton R. Vieira, Edgar Granados, Aravind Sivaramakrishnan, Marcio Gameiro, Konstantin Mischaikow, Kostas E. Bekris. Morse Graphs: Topological Tools for Analyzing the Global Dynamics of Robot Controllers. Proceedings of the 15th International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2022. [pdf] [arXiv]
- Aravind Sivaramakrishnan, Edgar Granados, Seth Karten, Troy McMahon, Kostas E. Bekris. Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers. Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021. [pdf] [arXiv]
Selected Workshop Papers
- Edgar Granados*, Aravind Sivaramakrishnan*, Troy McMahon, Zakary Littlefield, Kostas E. Bekris. ML4KP: a Light and Flexible Library for Development and Benchmarking of Sampling-Based Kinodynamic Planners. Machine Learning for Motion Planning workshop at ICRA 2021 (MLMP), 2021. [pdf]
- Aravind Sivaramakrishnan, Zakary Littlefield, Kostas E. Bekris. Towards Learning Efficient Maneuver Sets for Kinodynamic Motion Planning. 7th ICAPS Workshop on Planning and Robotics (PlanRob), 2019. [pdf]
Teaching
I have instructed / TAed for CS440: Introduction to Artificial Intelligence (Su17, Sp18, Sp19, Su19, Fa19, Su20, Sp21, Su21, Sp22, Fa23), CS520: Introduction to Artificial Intelligence (Sp17, Fa17, Fa19, Sp20, Fa20, Fa21, Fa22, Fa24, Sp24), CS460/560: Introduction to Computational Robotics (Fa18), and CS590: Socially Cognizant Robotics (Sp23).
Projects
- irl-lab
A WIP implementation of popular Inverse Reinforcement Learning algorithms for various tasks.
- Fake News Challenge
A better than baseline model written in Keras for the Fake News Challenge.
- Sampling Based Planners
A Python implementation of PRM and RRT for a simple 2D navigation task with polygonal obstacles.
- Leap Motion Data Recorder
A minimal C++ based solution for recording and playback of hand tracking data recorded using the Leap Motion controller.
- Deep Averaging Networks
A Keras implementation of the model described in this paper for factoid question answering.
- Vanilla GANs
A numpy implementation of fully connected Generative Adversarial Networks (GANs) on the MNIST dataset.
Misc
- Reviewer for: R:SS, T-RO, ICRA, IROS, RA-L, WAFR, ICAPS
- Attended the 3rd Summer School on Cognitive Robotics, held at USC in Los Angeles.
- Presented a tutorial on data-driven controllers for dynamical robotic systems as part of the TRIPODS Data Science Boot Camp: Dynamics, Data Analysis, and Robotic Control.
- Served as the Treasurer (2021-22) of the Computer Science Graduate Student Society (CSGSS).