13 citations · 19 across the 19 of their papers we have counts for
20 papers
Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
Xi Zheng, Dulanga Weerakoon, Yintong Huo +8
Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Br…
An LLM-driven Scenario Generation Pipeline Using an Extended Scenic DSL for Autonomous Driving Safety Validation
Fida Khandaker Safa, Yupeng Jiang, Xi Zheng
Real-world crash reports, which combine textual summaries and sketches, are valuable for scenario-based testing of autonomous driving systems (ADS). However, current methods cannot…
Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments
Jiaohong Yao, Linfeng Liang, Yao Deng +3
Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibili…
Towards autonomous normative multi-agent systems for Human-AI software engineering teams
Hoa Khanh Dam, Geeta Mahala, Rashina Hoda +2
This paper envisions a transformative paradigm in software engineering, where Artificial Intelligence, embodied in fully autonomous agents, becomes the primary driver of the core s…
Experiences from Benchmarking Vision-Language-Action Models for Robotic Manipulation
Yihao Zhang, Yuankai Qi, Xi Zheng
Foundation models applied in robotics, particularly \textbf{Vision--Language--Action (VLA)} models, hold great promise for achieving general-purpose manipulation. Yet, systematic r…
AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems
Linfeng Liang, Chenkai Tan, Yao Deng +3
Autonomous Driving Systems (ADS) are safety-critical, where failures can be severe. While Metamorphic Testing (MT) is effective for fault detection in ADS, existing methods rely he…
Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
Changfu Xu, Jianxiong Guo, Yuzhu Liang +7
Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and tr…
Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment
Weixian Qian, Tianyi Yang, Sebastian Schroder +5
Reliable assessment of safe landing sites in unstructured environments is essential for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications such as delivery, inspe…
Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Yupeng Jiang, Shuaiyi Sun, Xi Zheng
Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration…
A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Yupeng Jiang, Yao Deng, Sebastian Schroder +7
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety…
Towards Robust Autonomous Landing Systems: Iterative Solutions and Key Lessons Learned
Sebastian Schroder, Yao Deng, Alice James +5
Uncrewed Aerial Vehicles (UAVs) have become a focal point of research, with both established companies and startups investing heavily in their development. This paper presents our…
SAFE: Harnessing LLM for Scenario-Driven ADS Testing from Multimodal Crash Data
Siwei Luo, Yang Zhang, Yao Deng +2
Ensuring the safety of Autonomous Driving Systems (ADS) requires realistic and reproducible test scenarios, yet extracting such scenarios from multimodal crash reports remains a ma…
MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems
Linfeng Liang, Xi Zheng
Autonomous Driving Systems (ADSs) are safety-critical, as real-world safety violations can result in significant losses. Rigorous testing is essential before deployment, with simul…
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Jiwei Guan, Tianyu Ding, Longbing Cao +3
Vision-language pretraining (VLP) with transformers has demonstrated exceptional performance across numerous multimodal tasks. However, the adversarial robustness of these models h…
GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems
Linfeng Liang, Yao Deng, Kye Morton +7
Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, p…
CUEING: a lightweight model to Capture hUman attEntion In driviNG
Linfeng Liang, Yao Deng, Yang Zhang +4
Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby impr…
Scenario-Based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systems
Yao Deng, Xi Zheng, Mengshi Zhang +2
When developing autonomous driving systems (ADS), developers often need to replay previously collected driving recordings to check the correctness of newly introduced changes to th…
Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?
Guannan Lou, Yao Deng, Xi Zheng +2
Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional s…
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles
Jiwei Guan, Xi Zheng, Chen Wang +2
With recent advances in autonomous driving, Voice Control Systems have become increasingly adopted as human-vehicle interaction methods. This technology enables drivers to use voic…
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
Yao Deng, Xi Zheng, Tianyi Zhang +3
Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is als…