activity
20172026
collaborators

6 papers

cs.CR2026

MetaPermit: Scalable and Auditable Access Control for AI Agents via LLM-Inferred Meta-Attributes

Hanzhang Ma, Ali Hariri, Tianxiang Shen +9

The rise of autonomous AI agents equipped with tools has introduced significant security risks, ranging from unintended tool misuse to adversarial manipulation through Indirect Pro…

cs.RO2024

HE-Nav: A High-Performance and Efficient Navigation System for Aerial-Ground Robots in Cluttered Environments

Junming Wang, Zekai Sun, Xiuxian Guan +6

Existing AGR navigation systems have advanced in lightly occluded scenarios (e.g., buildings) by employing 3D semantic scene completion networks for voxel occupancy prediction and…

cs.RO2024

OMEGA: Efficient Occlusion-Aware Navigation for Air-Ground Robot in Dynamic Environments via State Space Model

Junming Wang, Xiuxian Guan, Zekai Sun +4

Air-ground robots (AGRs) are widely used in surveillance and disaster response due to their exceptional mobility and versatility (i.e., flying and driving). Current AGR navigation…

cs.RO2024

Hybrid-Parallel: Achieving High Performance and Energy Efficient Distributed Inference on Robots

Zekai Sun, Xiuxian Guan, Junming Wang +6

The rapid advancements in machine learning techniques have led to significant achievements in various real-world robotic tasks. These tasks heavily rely on fast and energy-efficien…

cs.RO2024

AGRNav: Efficient and Energy-Saving Autonomous Navigation for Air-Ground Robots in Occlusion-Prone Environments

Junming Wang, Zekai Sun, Xiuxian Guan +6

The exceptional mobility and long endurance of air-ground robots are raising interest in their usage to navigate complex environments (e.g., forests and large buildings). However,…

q-fin.RM2017

Research on ruin probability of risk model based on AR(1) series

Wenhao Li, Bolong Wang, Tianxiang Shen +2

In this text, we establish the risk model based on AR(1) series and propose the basic model which has a dependent structure under intensity of claim number. Considering some proper…