128 citations · 424 across the 59 of their papers we have counts for
61 papers
StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation
Xiao Liu, Yuguang Yang, Xi Wang +6
Generalist robot policies aim to map multimodal observations and linguistic task instructions to actions across diverse tasks. However, existing methods typically represent the fut…
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…
X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling
Baolu Li, Jingyu Qian, Rui Guo +17
Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and generalizable planning. Predictive…
Chat-Scene++: Exploiting Context-Rich Object Identification for 3D LLM
Haifeng Huang, Yilun Chen, Zehan Wang +2
Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods struggle with fine-grained object…
PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance
Yupeng Zheng, Xiang Li, Songen Gu +12
Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency and a lack of high-level know…
VistaBot: View-Robust Robot Manipulation via Spatiotemporal-Aware View Synthesis
Songen Gu, Yuhang Zheng, Weize Li +6
Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to…
Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving
Zhihua Hua, Junli Wang, Pengfei LI +6
Global navigation information and local scene understanding are two crucial components of autonomous driving systems. However, our experimental results indicate that many end-to-en…
StarVLA-: Reducing Complexity in Vision-Language-Action Systems
Jinhui Ye, Ning Gao, Senqiao Yang +7
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented…
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…
OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
Yuhang Zheng, Songen Gu, Yupeng Zheng +11
Contact-rich manipulation tasks, such as wiping and assembly, require accurate perception of contact forces, friction changes, and state transitions that cannot be reliably inferre…
Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
Hongjin Chen, Wei Zhang, Pengfei Li +10
Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environments. Although recent advancement…
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…
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…
Nimbus: A Unified Embodied Synthetic Data Generation Framework
Zeyu He, Yuchang Zhang, Yuanzhen Zhou +10
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acqui…
Enhancing Indoor Occupancy Prediction via Sparse Query-Based Multi-Level Consistent Knowledge Distillation
Xiang Li, Yupeng Zheng, Pengfei Li +3
Occupancy prediction provides critical geometric and semantic understanding for robotics but faces efficiency-accuracy trade-offs. Current dense methods suffer computational waste…
MM-ACT: Learn from Multimodal Parallel Generation to Act
Haotian Liang, Xinyi Chen, Bin Wang +12
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we…
World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild
Yupeng Zheng, Jichao Peng, Weize Li +22
We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale moti…
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…
Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
Qiao Sun, Liujia Yang, Wei Tang +12
While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity,…
InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation
Shuai Yang, Hao Li, Bin Wang +7
To operate effectively in the real world, robots should integrate multimodal reasoning with precise action generation. However, existing vision-language-action (VLA) models often s…
Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities
Liuyi Wang, Xinyuan Xia, Hui Zhao +6
Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deploym…
StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling
Meng Wei, Chenyang Wan, Xiqian Yu +9
Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low latency grounded in language instruct…