Charuvahan Adhivarahan
Multimodal Perception for Embodied Intelligence
Postdoctoral Associate
University at Buffalo
Table of Contents
Research Interests
Robots operating in unstructured environments need more than the ability to act — they need the perceptual grounding to understand what surrounds them, how it changes, and what it means for the task at hand. My research develops multimodal perception systems that fuse heterogeneous sensor streams — vision, depth, and wireless signals — into compact, learnable representations that robots can reason over in real time. A central theme is generalization: building representations and learning algorithms that remain reliable when the sensor configuration changes, the environment shifts, or knowledge must transfer across different robot embodiments. The underlying goal is perception that keeps robots genuinely autonomous over extended deployments — closing the gap between controlled laboratory performance and the varied, unpredictable conditions of the real world.
Projects
Gaussian Splatting the Physical World
In-Progress
Gaussian Splats have emerged as a compelling world representation for robotic perception, combining photorealism, geometric fidelity, and greater sample efficiency than point clouds. However, several challenges remain before they can serve as a robust perceptual backbone: the inherent smoothness of Gaussian primitives struggles with sharp boundaries critical for contact-rich manipulation; incremental online map updates remain underdeveloped; point correspondence and consistency across distributed multi-agent pipelines are non-trivial; and computational and memory demands remain prohibitive for real-time deployment.
Looking Beyond the Visible Spectrum
Solving the plastic waste crisis requires tools that can see what the human eye cannot. Our research harnesses invisible regions of the physical world — infrared light, thermal signatures, electrical charge, and dielectric fields — to unlock information hidden within plastic materials. By combining these non-visible sensing modalities with machine learning, we can identify plastic types with up to 100% accuracy and quantify recycled content with over 97% precision, without ever damaging the material being tested. Where conventional optical systems fall short, physics-informed sensing reveals the molecular fingerprints and thermal behaviors that define a material’s true composition — turning invisible signals into actionable data for a circular economy.
Sensing the World at Every Scale
In-Progress
Understanding complex environments requires more than any single sensor or vantage point can offer. Our research fuses data across the full electromagnetic spectrum — visible light, multispectral, near-infrared, shortwave infrared, and thermal — captured at scales ranging from orbital satellites to UAV aerial surveys to ground-level UGV scans. Rather than treating each modality in isolation, we leverage mutual information-based learning to allow models to teach one another, extracting shared structure across sensors and resolutions that no individual source could reveal alone. The result is a sensing framework where every wavelength and every altitude informs the whole — building richer, more robust models of the world beneath, above, and around us.
Publications
- Yaoli Zhao, Charuvahan Adhivarahan, Chandra Lekha Jyothula, Karthik Dantu, Thomas Thundat, and Amit Goyal, Determining the Percentage of Recycled Plastic Content in a Plastic Product, Communications Engineering, Vol. 5, No. 51, 2026.
- Charuvahan Adhivarahan and Karthik Dantu, WISDOM: WIreless Sensing-assisted Distributed Online Mapping, 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 8026–8033
- Zakieh S. Hashemifar, Charuvahan Adhivarahan, Anand Balakrishnan, and Karthik Dantu, Augmenting Visual SLAM with Wi-Fi Sensing for Indoor Applications, Autonomous Robots, Vol. 43, No. 8, 2019, pp. 2245–2260
- Charuvahan Adhivarahan, Zakieh Sadat Hashemifar, and Karthik Dantu, Improving RGB-D SLAM using Wi-Fi, 16th International Conference on Information Processing in Sensor Networks (IPSN), 2017
- Long Duong, Charuvahan Adhivarahan, Roshan Ayyalasomayajula, and Karthik Dantu. 2026. TIPS: Thermal Image based Plastics Sorting. In The 24th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys ’26), June 21–25, 2026, Cambridge, UK. ACM, New York, NY, USA. (Accepted)
- Zaid Tasneem, Charuvahan Adhivarahan, Dingkang Wang, Huikai Xie, Karthik Dantu, and Sanjeev J. Koppal, Adaptive Fovea for Scanning Depth Sensors, The International Journal of Robotics Research, Vol. 39, No. 7, 2020, pp. 837–855
- Stephen Xia, Minghui Zhao, Charuvahan Adhivarahan, Kaiyuan Hou, Yuyang Chen, Jingping Nie, Eugene Wu, Karthik Dantu, and Xiaofan Jiang, Anemoi: A Low-cost Sensorless Indoor Drone System for Automatic Mapping of 3D Airflow Fields, Proceedings of the 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom ’23), Article No. 77, 2023
- Yash Turkar, Pranay Meshram, Christo Aluckal, Charuvahan Adhivarahan, and Karthik Dantu, Empir3D: A Framework for Multi-Dimensional Point Cloud Assessment, arXiv preprint arXiv:2306.03660, 2023
- Yash Turkar, Christo Aluckal, Charuvahan Adhivarahan, Alessandro Sebastiani, and Karthik Dantu, A View-Planning Approach to 3D Reconstruction, IEEE International Conference on Extended Reality (XR), 2024, pp. 340–350
- Yash Turkar, Shaunak De, Charuvahan Adhivarahan, Luca Mottola, Alessandro Sebastiani, Davide Castelletti, and Karthik Dantu, Enhancing Archaeological Surveys with InSAR Imagery and UAV-Based GPR, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2024, pp. 11454–11456
- Kartikeya Singh, Yash Turkar, Christo Aluckal, Charuvahan Adhivarahan, and Karthik Dantu, PANOS: Payload-Aware Navigation in Offroad Scenarios, arXiv preprint arXiv:2409.16566, 2024
- Shiv Mehta, Vaishali Maheshkar, Charuvahan Adhivarahan, and Karthik Dantu, CLIPS: Continual Learning Infrastructure for Plastics Sorting, 2025 International Conference on Machine Learning and Applications (ICMLA), 2025, pp. 1197–1204
- Pranay Meshram, Yash Turkar, Kartikeya Singh, and Praveen Raj Masilamani, QAL: A Loss for Recall-Precision Balance in 3D Reconstruction, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026
- Vaishali Maheshkar, Aadarsh Anantha Ramakrishnan, Charuvahan Adhivarahan, and Karthik Dantu, Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting, arXiv preprint arXiv:2505.16513, 2025
- Pranay Meshram, Charuvahan Adhivarahan, Ehsan Tarkesh Esfahani, Souma Chowdhury, Chen Wang, and Karthik Dantu, CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments, arXiv preprint arXiv:2601.13361, 2026
Education
University at Buffalo, Buffalo, New York, USA
Ph.D., Computer Science, May 2023
Advisor: Dr. Karthik Dantu
M.S., Computer Science, May 2018
Annamalai University, Chidambaram, Tamilnadu, India
B.E., Computer Science, May, 2009
Academic Experience
University at Buffalo, Buffalo, New York, USA
Postdoctoral Associate, Computer Science. (Current)
Novel representations for Embodied Intelligence with multi-modal sensing.
Research Assistant January, 2018 - Aug 2023
Duties at various times have included research into high fidelity motion capture and maintaining the SMART Motion Capture Lab, training users on the mocap system and robots like the Baxter, URX arms and the Husky and consulting in design of experiment for motion capture.
Teaching Assistant August, 2017 - December, 2017
Duties at various times have included conducting office hours and recitations for CSE 468/568 Intro to Robotics Algorithms and CSE 487/587 Data Intensive computing. Topics include: kinematics, probabilistic algorithms for localization and mapping, planning, and navigation for Robotics Algorithms and MapReduce and predictive analytics, statistical software packages in R and Python and big data infrastructures like Hadoop and Spark ecosystems for Data Intensive Computing
Professional Experience
HCL Technologies, Chennai, Tamilnadu India
Senior Software Engineer Jan, 2010 - Feb, 2014
Engineered several projects, including end-to-end development and maintainance of shopping application with database design, web services, front-ends for the web, phone and television
Contact Information
Computer Skills
- Frameworks: ROS, Tensorflow, PyTorch
- Python, C, C++, C\#, Java, JavaScript
- Algorithms: Simultaneous Localization and Mapping packages, Task Allocation, Multi-agent Task Planning and Reinforcement Learning
- Operating Systems: GNU/Linux, Windows and MacOS