World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. However, their multi-view interfaces typically tile images or concatenate tokens, leaving the geometric relationships among synchronized cameras implicit. This makes it...
Wen-Bo Chen, Tian-Fu Li, Hao-Xuan Xu et al.· 0 citations
Vision-Language-Action models are increasingly effective for robotic manipulation, yet most predict actions directly from current observations without explicitly modeling future scene evolution. Recent methods introduce future prediction to improve action generation, but dense future modeling often requires expensive i...
Tian-Fu Li, Hao-Xuan Xu, Wen-Bo Chen et al.· 0 citations
This work designs a hybrid architecture that combines sparse anchors with an offset refinement module for efficient multi-modal trajectory prediction and adopts a two-stage training paradigm consisting of pretraining and reward-guided fine-tuning, demonstrating superior performance under real-time constraints.
Cheng-Lin Chen, Lu-Jia Wang, Xin-Hu Zheng et al.· 0 citations
LIRA is introduced, a local cross-layer action-conditioning mechanism that formulates VLM-to-action conditioning as depth-aware information routing and improves the principal aggregate metrics over the VLA-Adapter baseline under the same 0.5B-parameter configuration.
Zhe-Wei Zhang, Pu Wang, Guan-Ren Qiao et al.· 4 citations
DreamTrajectory is presented, a trajectory-guided framework for language-conditioned mobile manipulation that introduces one component for each limitation of existing Vision-Language-Action policies, and jointly predicts an intention-level end-effector trajectory and a whole-body action chunk in a single action expert.
Zheng Yang, Wen-Jie Zhang, Xiang-Yu Chen et al.· 1 citation
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