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FASA: Feedback-Aware Sampling Adaptation for Efficient Diffusion-Based VLA Models
Yuchen Han, Jianhan Wu, Xiaoyang Qu +3
Diffusion-based Vision-Language-Action (VLA) models achieve strong performance in embodied tasks, but their iterative sampling imposes heavy computational and memory-access cost, b…
Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Jianzong Wang, Botao Zhao, Yayun He +2
Achieving general-purpose robotics requires empowering robots to adapt and evolve based on their environment and feedback. Traditional methods face limitations such as extensive tr…
Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation
Yayun He, Zuheng Kang, Botao Zhao +3
Vision-language models (VLMs) have significantly improved the generalization capabilities of robotic manipulation. However, VLM-based systems often suffer from a lack of robustness…
From Knowing to Doing Precisely: A General Self-Correction and Termination Framework for VLA models
Wentao Zhang, Aolan Sun, Wentao Mo +3
While vision-language-action (VLA) models for embodied agents integrate perception, reasoning, and control, they remain constrained by two critical weaknesses: first, during graspi…
CARE: Multi-Task Pretraining for Latent Continuous Action Representation in Robot Control
Jiaqi Shi, Xulong Zhang, Xiaoyang Qu +1
Recent advances in Vision-Language-Action (VLA) models have shown promise for robot control, but their dependence on action supervision limits scalability and generalization. To ad…