8 papers
PRIMO: Prior-Informed Odometry from Human-Motion Tracking for Humanoid Robots
Xu Han, Angsong Li, Shaopeng Zhang +5
Simulation-trained humanoid proprioceptive odometry faces two transfer challenges: training trajectories generated by specific robot control policies intended for deployment cover…
Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models
Xiatao Sun, Yuan Zhuang, Mateo Sanchez Lopez Negrete +9
Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in scene layout, lighting, or nearby…
Toward Open-World Video Segmentation over Long Horizons
Qing Su, Kaiyang Li, Yuan Zhuang +2
Long videos challenge open-world segmentation systems to continually discover objects and preserve their identities through disappearance and reappearance. Savvy, a zero-shot, semi…
Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems
Zejian Eric Wu, Zhongyi Jiang, Yuan Zhuang +1
Multi-agent systems are increasingly deployed to support various tasks where agents interact to achieve individual and collective objectives. Although these systems can enhance tas…
SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection
Tianchonghui Fang, Yuan Zhuang, Fei Miao
Vision-Language-Action (VLA) and imitation-learning policies trained via community toolchains on low-cost hardware frequently fail when deployed outside the training environment. E…
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Yuan Zhuang, Yuexin Bian, Sihong He +7
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and i…
LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts
Yuan Zhuang, Yi Shen, Yuexin Bian +4
Recent studies have shown that combining parameter-efficient fine-tuning (PEFT) with mixture-of-experts (MoE) is an effective strategy for adapting large language models (LLMs) to…
YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning
Yuan Zhuang, Yi Shen, Zhili Zhang +2
Advancements in deep multi-agent reinforcement learning (MARL) have positioned it as a promising approach for decision-making in cooperative games. However, it still remains challe…