Overview
This research direction explores the application of Conformal Prediction techniques in the field of robotics. Conformal Prediction provides a framework for quantifying uncertainty in predictions, which is crucial for robotic systems operating in dynamic and uncertain environments. While these techniques have been widely studied in machine learning, their application to robotics is still an emerging area of research. This work aims to bridge that gap by developing Conformal Prediction methods tailored for robotic applications, such as perception, control, and decision-making.
Data-Driven Non-Conformity Scores for Conformal Prediction
One critical problem in Conformal Prediction is the design of non-conformity scores, which measure how well a new data point conforms to the training data. Traditional approaches often rely on simple distance metrics or residuals, which may not capture the complex relationships in robotic data. This research direction focuses on developing data-driven non-conformity scores that leverage machine learning models to better capture the underlying structure of the data, leading to more accurate and informative prediction sets.
A python library for data-driven non-conformity scores is available at conformal_region_designer
- Physics Constrained Motion Prediction with Uncertainty Quantification
Renukanandan Tumu, Lars Lindemann, Truong X. Nghiem, Rahul Mangharam
Intelligent Vehicles (IV), 2023 - Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates
Renukanandan Tumu*, Matthew Cleaveland*, Rahul Mangharam, George Pappas, Lars Lindemann
Learning for Dynamics and Control, 2024 - AdaptNC: Adaptive Nonconformity SCores for Uncertainty-Aware Autonomous Systems in Dynamic Environments
Renukanandan Tumu, Aditya Singh, Rahul Mangharam
Preprint, 2026
Conformal Prediction in Multi-Agent Settings
Another critical problem in the application of Conformal Prediction to robotics is overcoming the distribution shifts that arise in multi-agent settings. In such scenarios, the behavior of one agent can significantly influence the environment and the data distribution observed by other agents. This research direction focuses on developing Conformal Prediction methods that can adapt to these distribution shifts, ensuring that the prediction sets remain valid and informative even as the interactions between agents evolve.
- Conformal Off-Policy Prediction for Multi-Agent Systems
Tom Kuipers*, Renukanandan Tumu*, Shuo Yang, Milad Kazemi, Rahul Mangharam, Nicola Paoletti Conference on Decision and Control (CDC), 2024 - AdaptNC: Adaptive Nonconformity SCores for Uncertainty-Aware Autonomous Systems in Dynamic Environments
Renukanandan Tumu, Aditya Singh, Rahul Mangharam
Preprint, 2026
Citation
@INPROCEEDINGS{10186812,
author={Tumu, Renukanandan and Lindemann, Lars and Nghiem, Truong and Mangharam, Rahul},
booktitle={2023 IEEE Intelligent Vehicles Symposium (IV)},
title={Physics Constrained Motion Prediction with Uncertainty Quantification},
year={2023},
volume={},
number={},
pages={1-8},
keywords={Uncertainty;Heuristic algorithms;Dynamics;Predictive models;Robustness;Trajectory;Safety;motion-prediction;autonomous-driving;conformal-prediction;physics-constrained;machine-learning},
doi={10.1109/IV55152.2023.10186812}}
@INPROCEEDINGS{10886791,
author={Kuipers, Tom and Tumu, Renukanandan and Yang, Shuo and Kazemi, Milad and Mangharam, Rahul and Paoletti, Nicola},
booktitle={2024 IEEE 63rd Conference on Decision and Control (CDC)},
title={Conformal Off-Policy Prediction for Multi-Agent Systems},
year={2024},
volume={},
number={},
pages={1067-1074},
keywords={Prediction methods;Switches;Probabilistic logic;Libraries;Trajectory;Complexity theory;Calibration;Reliability;Standards;Multi-agent systems},
doi={10.1109/CDC56724.2024.10886791}}
@InProceedings{pmlr-v242-tumu24a,
title = {Multi-modal conformal prediction regions by optimizing convex shape templates},
author = {Tumu, Renukanandan and Cleaveland, Matthew and Mangharam, Rahul and Pappas, George and Lindemann, Lars},
booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference},
pages = {1343--1356},
year = {2024},
editor = {Abate, Alessandro and Cannon, Mark and Margellos, Kostas and Papachristodoulou, Antonis},
volume = {242},
series = {Proceedings of Machine Learning Research},
month = {15--17 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v242/tumu24a/tumu24a.pdf},
url = {https://proceedings.mlr.press/v242/tumu24a.html},
abstract = {Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal prediction algorithms is a non-conformity score function that quantifies how different a model’s prediction is from the unknown ground truth value. Essentially, these functions determine the shape and the size of the conformal prediction regions. However, little work has gone into finding non-conformity score functions that produce prediction regions that are multi-modal and practical, i.e., that can efficiently be used in engineering applications. We propose a method that optimizes parameterized shape template functions over calibration data, which results in non-conformity score functions that produce prediction regions with minimum volume. Our approach results in prediction regions that are multi-modal, so they can properly capture residuals of distributions that have multiple modes, and practical, so each region is convex and can be easily incorporated into downstream tasks, such as a motion planner using conformal prediction regions. Our method applies to general supervised learning tasks, while we illustrate its use in time-series prediction. We provide a toolbox and present illustrative case studies of F16 fighter jets and autonomous vehicles, showing an up to 68% reduction in prediction region area.}
}
@misc{tumu2025adversarialsocialinfluencemodeling,
title={Adversarial Social Influence: Modeling Persuasion in Contested Social Networks},
author={Renukanandan Tumu and Cristian Ioan Vasile and Victor Preciado and [Rahul Mangharam](/team/rahul)},
year={2025},
eprint={2510.01481},
archivePrefix={arXiv},
primaryClass={cs.SI},
url={https://arxiv.org/abs/2510.01481},
}
