18 papers · 1 filter
Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer
Zetao Cai, Yaping Li, Yiqun Wang +8
Robot demonstrations are expensive to collect and often provide limited distributional coverage of task variations. Human videos offer a low-cost source of complementary manipulati…
RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation
Ziqin Wang, Hao Li, Weijun Wang +5
Existing robot datasets remain expensive to curate, embodiment-specific, and insufficiently annotated with the fine-grained structure required for generalizable reasoning, executio…
Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
Jiaqi Peng, Xiqian Yu, Delin Feng +10
While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying s…
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…
FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model
Xiaoxu Xu, Hao Li, Jinhui Ye +7
Predictive foresight is important to intelligent embodied agents. Since the motor execution of a robot is intrinsically constrained by its visual perception of environmental geomet…
UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data
Sizhe Yang, Yiman Xie, Zhixuan Liang +4
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the…
InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
Junhao Cai, Zetao Cai, Jiafei Cao +39
Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, b…
ST4VLA: Spatially Guided Training for Vision-Language-Action Models
Jinhui Ye, Fangjing Wang, Ning Gao +9
Large vision-language models (VLMs) excel at multimodal understanding but fall short when extended to embodied tasks, where instructions must be transformed into low-level motor ac…
DexImit: Learning Bimanual Dexterous Manipulation from Monocular Human Videos
Juncheng Mu, Sizhe Yang, Yiming Bao +6
Data scarcity fundamentally limits the generalization of bimanual dexterous manipulation, as real-world data collection for dexterous hands is expensive and labor-intensive. Human…
RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation
Hao Li, Ziqin Wang, Zi-han Ding +9
Advances in large vision-language models (VLMs) have stimulated growing interest in vision-language-action (VLA) systems for robot manipulation. However, existing manipulation data…
Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction
Sizhe Yang, Linning Xu, Hao Li +4
3D spatial perception is fundamental to generalizable robotic manipulation, yet obtaining reliable, high-quality 3D geometry remains challenging. Depth sensors suffer from noise an…
InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy
Yang Tian, Yuyin Yang, Yiman Xie +13
Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of…
Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning
Weijie Shen, Yitian Liu, Yuhao Wu +10
Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presen…
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
Xinyi Chen, Yilun Chen, Yanwei Fu +26
We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its…
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…
A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning
Shaopeng Zhai, Qi Zhang, Tianyi Zhang +7
Robotic real-world reinforcement learning (RL) with vision-language-action (VLA) models is bottlenecked by sparse, handcrafted rewards and inefficient exploration. We introduce VLA…
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Haozhan Li, Yuxin Zuo, Jiale Yu +18
Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and super…
F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Qi Lv, Weijie Kong, Hao Li +7
Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt rea…