4 papers
X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
Jinliang Zheng, Jianxiong Li, Zhihao Wang +12
Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To…
Efficient Robotic Policy Learning via Latent Space Backward Planning
Dongxiu Liu, Haoyi Niu, Zhihao Wang +6
Current robotic planning methods often rely on predicting multi-frame images with full pixel details. While this fine-grained approach can serve as a generic world model, it introd…
Universal Actions for Enhanced Embodied Foundation Models
Jinliang Zheng, Jianxiong Li, Dongxiu Liu +7
Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe for building embodied agents has faced noticea…
Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learning
Jianxiong Li, Zhihao Wang, Jinliang Zheng +8
Multimodal task specification is essential for enhanced robotic performance, where \textit{Cross-modality Alignment} enables the robot to holistically understand complex task instr…