29 citations · 70 across the 41 of their papers we have counts for
13 papers · 1 filter
Data Pyramid for Embodied Manipulation: A Survey
Yifan Ye, Yankai Fu, Yaoxu Lv +26
Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations w…
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Yunfan Lou, Yifan Ye, Yankai Fu +7
World action models inherit the predictive capability of world models, enabling action generation to be guided by anticipated future observations. However, they rely primarily on v…
RynnBrain: Open Embodied Foundation Models
Ronghao Dang, Jiayan Guo, Bohan Hou +23
Despite rapid progress in multimodal foundation models, embodied intelligence community still lacks a unified, physically grounded foundation model that integrates perception, reas…
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
Tianshuai Hu, Xiaolu Liu, Song Wang +17
Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-ta…
Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios
Zeyi Liu, Shuang Liu, Jihai Min +7
With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has…
Token Expand-Merge: Training-Free Token Compression for Vision-Language-Action Models
Yifan Ye, Jiaqi Ma, Jun Cen +1
Vision-Language-Action (VLA) models pretrained on large-scale multimodal datasets have emerged as powerful foundations for robotic perception and control. However, their massive sc…
VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer
Songqiao Hu, Zeyi Liu, Shuang Liu +5
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in generalizing across diverse robotic manipulation tasks. However, deploying these models in unstruct…
HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data
Ruizhe Liu, Pei Zhou, Qian Luo +4
Effective generalization in robotic manipulation requires representations that capture invariant patterns of interaction across environments and tasks. We present a self-supervised…
RynnVLA-002: A Unified Vision-Language-Action and World Model
Jun Cen, Siteng Huang, Yuqian Yuan +11
We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict future image states, learning the un…
Self-evolved Imitation Learning in Simulated World
Yifan Ye, Jun Cen, Jing Chen +1
Imitation learning has been a trend recently, yet training a generalist agent across multiple tasks still requires large-scale expert demonstrations, which are costly and labor-int…
Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
Haoyu Zhao, Linghao Zhuang, Xingyue Zhao +10
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level…
WorldVLA: Towards Autoregressive Action World Model
Jun Cen, Chaohui Yu, Hangjie Yuan +9
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model an…
Generative Artificial Intelligence in Robotic Manipulation: A Survey
Kun Zhang, Peng Yun, Jun Cen +11
This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulati…