12 papers
JEPA Policy: Diffusion-Free Imitation Learning via Paired Action and Future Representation Prediction
Jie Xu, Kangjin Yu, Ziyi Jin +5
Standard behavior cloning supervises actions without explicitly constraining the future representation paired with each demonstrated action chunk. We introduce JEPA Policy, a diffu…
GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation
Yupeng Zheng, Xiang Li, Songen Gu +11
Vision-language pre-training and predictive world modeling provide robot policies with rich semantic and dynamic visual features, but their native action and visual-prediction obje…
Latent Action as Intention Enables Efficient Future Imagination for World Action Models
Xiang Li, Yupeng Zheng, Songen Gu +11
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes t…
NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation
Cong Zhao, Shuai Tian, Xu Zhang +11
Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness.…
WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
Jiahao Liu, Zhongpu Xia, Shuai Tian +13
Generalizable robot policies typically rely on action-labeled robot demonstrations, which are expensive to collect and difficult to scale. In contrast, large-scale human and robot…
VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
Shuai Tian, Yupeng Zheng, Yuhang Zheng +7
Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction, yet these cues are temporally sparse and often invisible in visual observat…