2 citations · 3 across the 7 of their papers we have counts for
7 papers
UniLM-Nav: A Unified Framework for Zero-Shot Last-Mile Navigation
Zhuofan Zhang, Tianxu Wang, Guoxi Zhang +6
Mobile manipulation requires a robot to navigate to a target object or receptacle and then perform intended manipulation. However, reaching the vicinity of the target does not guar…
SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
Lirui Luo, Guoxi Zhang, Hongming Xu +2
In deep reinforcement learning (DRL), an agent is trained from a stream of experience. In a continual learning setting, such agents can suffer from plasticity loss: their ability t…
AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark
Hongxin Li, Xiping Wang, Jingran Su +4
Autonomous agents capable of navigating Graphical User Interfaces (GUIs) hold the potential to revolutionize digital productivity. However, achieving true digital autonomy extends…
Hypothesis Graph Refinement: Hypothesis-Driven Exploration with Cascade Error Correction for Embodied Navigation
Peixin Chen, Guoxi Zhang, Jianwei Ma +1
Embodied agents must explore partially observed environments while maintaining reliable long-horizon memory. Existing graph-based navigation systems improve scalability, but they o…
MVR: Multi-view Video Reward Shaping for Reinforcement Learning
Lirui Luo, Guoxi Zhang, Hongming Xu +3
Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language mod…
SYNERGAI: Perception Alignment for Human-Robot Collaboration
Yixin Chen, Guoxi Zhang, Yaowei Zhang +4
Recently, large language models (LLMs) have shown strong potential in facilitating human-robotic interaction and collaboration. However, existing LLM-based systems often overlook t…
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
Lirui Luo, Guoxi Zhang, Hongming Xu +3
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies. NS-RL…