3 papers
cs.AI2026
Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments
Guo Gan, Yilun Zhao, Cong Chen +5
GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack systematic evaluation of agent…
cs.LG2026
Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions
Guo Gan, Yuxuan Ding, Cong Chen +3
Online reinforcement learning (RL) serves as an effective method for enhancing the capabilities of Android agents. However, guiding agents to learn through online interaction is pr…
cs.AI2025
Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations
Shaoshan Liu, Fan Wang, Hongjun Zhou +1
While theory and practice are often seen as separate domains, this article shows that theoretical insight is essential for overcoming real-world engineering barriers. We begin with…