5 papers
SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation
Changyuan Wang, Chubin Zhang, Zhenyu Wu +8
Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. How…
MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models
Yue Wu, Changyuan Wang, Zixuan Wang +2
Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution heterogeneity and disparate outli…
SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation
Shilin Ma, Chubin Zhang, Changyuan Wang +6
Real-time inference of vision-language-action (VLA) models is essential for robotic control. While visual token pruning has shown strong potential for accelerating inference, most…
MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation
Runhao Li, Wenkai Guo, Zhenyu Wu +5
Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these mode…
Q-VLM: Post-training Quantization for Large Vision-Language Models
Changyuan Wang, Ziwei Wang, Xiuwei Xu +3
In this paper, we propose a post-training quantization framework of large vision-language models (LVLMs) for efficient multi-modal inference. Conventional quantization methods sequ…