Amit Ranjan Trivedi — accepted AI-conference papers

2026

EigenShield: Inference-Time, Model-Agnostic Jailbreaking Defense via Causal Subspace Filtering

Nastaran Darabi, Devashri Naik, Sina Tayebati, Dinithi Jayasuriya, Ranganath Krishnan, Amit Ranjan Trivedi

AAAI 2026technical

Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to adversarial attacks despite widespread adoption. Existing defenses typically require retraining, rely on heuristics, or fail under adaptive and out-of-distribution (OOD) conditions. We introduce EigenShield, a

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2026

Learnable Conformal Prediction for Safe and Efficient Robotics under Perception and Planning Uncertainties

Divake Kumar, Sina Tayebati, Francesco Migliarba, Ranganath Krishnan, Amit Ranjan Trivedi

ICRA 2026poster

Deep learning models in robotics often output point estimates with poorly calibrated confidences, offering no native mechanism to quantify predictive reliability under novel, noisy, or out-of-distribution inputs. Conformal prediction (CP) addresses this gap by providing distribution-free coverage gu…

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2026

Resilience in Ambient Multi-Agent LLMs via Decentralized Bio-Autonomic Control and Immune-Inspired Anomaly Detection

Nastaran Darabi, Devashri Naik, Sina Tayebati, Dinithi Jayasuriya, Amit Ranjan Trivedi

AAAI 2026technical

Large Language Model (LLM) agents are now widely deployed in Ambient Intelligence (AmI) environments, where autonomous agents must sense, act, and coordinate at scale. As agent capabilities and interdependence increase, traditional reliability strategies such as isolated adaptive control, anomaly de

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2025

Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through Curriculum Training

Nastaran Darabi, Dinithi Jayasuriya, Devashri Naik, Theja Tulabandhula, Amit Ranjan Trivedi

ICRA 2025

Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack surface, making 3D vision particularly vulnerable

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2025

Generative Sensing: Pre-training LiDAR with Masked Autoencoders for Ultra-Frugal Perception

Sina Tayebati, Theja Tulabandhula, Amit Ranjan Trivedi

ICASSP 2025accepted

We propose a disruptively frugal generative sensing approach for LiDAR that generates, rather than senses, parts of the environment that are either predictable based on extensive training or have limited impact on overall prediction accuracy. Our generative pre-training strategy for this purpose, ra…

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2025

Uncertainty-Aware Deep Reinforcement Learning with Calibrated Quantile Regression and Evidential Learning

Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula, Mohit Mittal, Amit Ranjan Trivedi

ICRA 2025

We present a novel statistical approach to incorporate uncertainty awareness in model-free distributional deep reinforcement learning for mission and safety-critical robotics. Deep learning predictions are influenced by uncertainties in the data, termed as aleatoric uncertainties, as well as uncerta

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2024

Conformalized Multimodal Uncertainty Regression and Reasoning

Domenico Parente, Nastaran Darabi, Alex C. Stutts, Theja Tulabandhula, Amit Ranjan Trivedi

ICASSP 2024accepted

This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning regressor. We specifically discuss its application for visual odometry (VO), where environmental features such as flying d…

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2024

Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge

Alex C. Stutts, Danilo Erricolo, Sathya Ravi, Theja Tulabandhula, Amit Ranjan Trivedi

ICRA 2024poster

In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance. Three-dimensional (3D) object detection, a critical robotics operation, has seen significant advancements; however, the majority of current works focus only on accuracy and ig…

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2023

Lightweight, Uncertainty-Aware Conformalized Visual Odometry

Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula, Amit Ranjan Trivedi

IROS 2023poster

Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex environments. However, emerging autonomous edge robotics devices like insect-scale drones and surgical robots lack a comp…

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2023

Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies

Priyesh Shukla, Sureshkumar S., Alex C. Stutts, Sathya Ravi, Theja Tulabandhula, Amit Ranjan Trivedi

ICASSP 2023accepted

We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predictions with respect to model scalability and tolerance to env…

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