5 papers
Policy Contrastive Decoding for Robotic Foundation Models
Shihan Wu, Xu Luo, Ji Zhang +4
Robotic foundation models, or generalist robot policies, hold immense potential to enable flexible, general-purpose and dexterous robotic systems. Despite their advancements, our e…
RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation
Shihan Wu, Xuecheng Liu, Shaoxuan Xie +81
Despite the critical role of bimanual manipulation in endowing robots with human-like dexterity, large-scale and diverse datasets remain scarce due to the significant hardware hete…
InSpire: Vision-Language-Action Models with Intrinsic Spatial Reasoning
Ji Zhang, Shihan Wu, Xu Luo +4
Leveraging pretrained Vision-Language Models (VLMs) to map language instruction and visual observations to raw low-level actions, Vision-Language-Action models (VLAs) hold great pr…
A Closer Look at Conditional Prompt Tuning for Vision-Language Models
Ji Zhang, Shihan Wu, Lianli Gao +3
Despite the great promise of Prompt Tuning (PT) in adapting large Vision-Language Pretrained Models (VLPMs) to downstream tasks, they often struggle to overcome the Base-New Tradeo…
Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves
Shihan Wu, Ji Zhang, Pengpeng Zeng +3
Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning…