18 papers
Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control
Weili Zeng, Yitong Xing, Fulong Liu +10
The paper introduces Enfold, a method that folds the computation of a world-generative model into a predictive representation derived from the current visual scene and language ins…
Exploratory, Communicative, and Deployable: Vision-Driven Embodied Agents for Open-World Mobile Manipulation
Boyu Mi, Mengchen Ma, Yifei Yao +10
The paper introduces REAL, a framework that trains embodied agents for open‑world mobile manipulation using sim‑to‑real consistent environments, hierarchical training, and human‑in…
-EqM: Equilibrium Matching for Closed-Loop Vision-Language-Action Control
Huanming Liu, Congsheng Xu, Jianmin Ji +1
Currently, Vision-Language-Action (VLA) models have become the most adopted paradigm for robotic manipulation for its great potential for task generalization. While most generative…
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In pract…
DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
Zili Lin, Wenyao Zhang, Yuyang Zhang +9
Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation due to two challenges: (1) the…
LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models
Rongxu Cui, Zongzheng Zhang, Jingrui Pang +11
Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains largely unverified. To address th…