6 citations · 6 across the 9 of their papers we have counts for
11 papers
Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang +5
We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using only ten annotated images f…
MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation
Yang Liu, Pengxiang Ding, Tengyue Jiang +10
Vision-Language-Action (VLA) models map visual observations and natural-language instructions to robot actions; however, hierarchical and autoregressive paradigms often incur archi…
Graph-Enhanced Policy Optimization in LLM Agent Training
Jiazhen Yuan, Zhike Gong, Jinquan Hang +2
Multi-step LLM agents in interactive environments represent a crucial step toward long-horizon decision-making. To train such agents, group-based reinforcement learning is widely a…
Towards a Unified Understanding of Robot Manipulation: A Comprehensive Survey
Shuanghao Bai, Wenxuan Song, Jiayi Chen +15
Embodied intelligence has witnessed remarkable progress in recent years, driven by advances in computer vision, natural language processing, and the rise of large-scale multimodal…
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Yihao Wang, Pengxiang Ding, Lingxiao Li +13
Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a large-scale Vision-Language Model (VLM) on robotic data. While t…
Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions
Wei Zhao, Gongsheng Li, Zhefei Gong +3
Vision-Language-Action (VLA) models have recently become highly prominent in the field of robotics. Leveraging vision-language foundation models trained on large-scale internet dat…
PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
Wenxuan Song, Jiayi Chen, Pengxiang Ding +9
Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with actio…
VLAS: Vision-Language-Action Model With Speech Instructions For Customized Robot Manipulation
Wei Zhao, Pengxiang Ding, Min Zhang +4
Vision-language-action models (VLAs) have become increasingly popular in robot manipulation for their end-to-end design and remarkable performance. However, existing VLAs rely heav…
Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation
Shuanghao Bai, Wanqi Zhou, Pengxiang Ding +3
Behavior Cloning (BC) is a widely adopted visual imitation learning method in robot manipulation. Current BC approaches often enhance generalization by leveraging large datasets an…
CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction
Zhefei Gong, Pengxiang Ding, Shangke Lyu +5
In robotic visuomotor policy learning, diffusion-based models have achieved significant success in improving the accuracy of action trajectory generation compared to traditional au…
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
Han Zhao, Min Zhang, Wei Zhao +3
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the foundation model for many downstream…