14 papers
MaskVLA: Visual Masking Against Trajectory Overfitting of Vision-Language-Action Model
Yuxuan Jiang, Jiaying Huang, Ge Wang +9
Vision-Language-Action (VLA) models integrate vision-language understanding with executable robot actions, enabling end-to-end learning for robot control. However, our empirical an…
Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments
Ruochu Yang, Ziyi Xia, Huibo Zhang +6
Multi-object search in unknown household environments requires planning under extensive uncertainty - from unknown object locations to cluttered spaces with unobserved obstacles. P…
ReFPO: Reflow Regularization for Flow Matching Policy Gradients
Ge Wang, Yibo Peng, Fan Feng +10
We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for efficient flow-based control. We…
MimicIK: Real-Time Generative Inverse Kinematics from Teleoperation with FK Consistency
Jiahao Yang, Shenhao Yan, Fan Feng +5
Inverse kinematics (IK) remains a critical bottleneck for real-time robot manipulation. Classical numerical solvers achieve high geometric precision but often suffer from discontin…
Acting While Understanding: Asynchronous Semantic-Action Decoupling for Real-Time Vision-Language-Action Models
Shenhao Yan, Ge Wang, Qi Liu +7
Vision-Language-Action models (VLAs) have demonstrated strong task understanding and generalization in robotic manipulation, yet the high computational cost of full-model inference…
Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models
Ge Wang, Xinyu Tan, Xiang Li +11
Vision-language-action (VLA) models are powerful action generators for robot manipulation, but they are typically executed with fixed inference and replanning schedules. This rigid…
KoopmanFlow: Spectrally Decoupled Generative Control Policy via Koopman Structural Bias
Chengsi Yao, Ge Wang, Kai Kang +9
Generative Control Policies (GCPs) show immense promise in robotic manipulation but struggle to simultaneously model stable global motions and high-frequency local corrections. Whi…
SVLL: Staged Vision-Language Learning for Physically Grounded Embodied Task Planning
Yuyuan Yang, Junkun Hong, Hongrong Wang +11
Embodied task planning demands vision-language models to generate action sequences that are both visually grounded and causally coherent over time. However, existing training parad…
RoboSeek: You Need to Interact with Your Objects
Yibo Peng, Jiahao Yang, Shenhao Yan +5
Optimizing and refining action execution through exploration and interaction is a promising way for robotic manipulation. However, practical approaches to interaction-driven roboti…
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
Mingcong Lei, Honghao Cai, Yuyuan Yang +16
Embodied intelligence aims to enable robots to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with parti…
Empowering Large Language Models with 3D Situation Awareness
Zhihao Yuan, Yibo Peng, Jinke Ren +7
Driven by the great success of Large Language Models (LLMs) in the 2D image domain, their applications in 3D scene understanding has emerged as a new trend. A key difference betwee…
CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic Environments
Mingcong Lei, Ge Wang, Yiming Zhao +7
Large Language Models (LLMs) exhibit remarkable capabilities in the hierarchical decomposition of complex tasks through semantic reasoning. However, their application in embodied s…
STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning
Mingcong Lei, Yiming Zhao, Ge Wang +4
A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability. To achi…
Adaptive Pruning for Large Language Models with Structural Importance Awareness
Haotian Zheng, Jinke Ren, Yushan Sun +6
The recent advancements in large language models (LLMs) have significantly improved language understanding and generation capabilities. However, it is difficult to deploy LLMs on r…