12 papers
PS-DLO: Property- and Spatially Aware Deformable Linear Object Manipulation Policy
Mengdi Li, Boxian Yao, Xiyu Zhu +5
Deformable linear objects (DLO) pose a coupled perception and control challenge: their slender geometry makes precise localization difficult, while their deformations depend on obj…
ForceBench: Benchmarking Force Integration for Robotic Manipulation
Xuan Han, Zinuo Jin, Tianlin Li +8
Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution. However, differences in tasks, demonstration…
Token-World: World Modeling in Vision-Language Model Token Space for Robot Manipulation
Chuyao Fu, Xiaowei Chi, Yuhan Rui +14
A common approach to world-model simulation for vision-language-action (VLA) systems is to predict future RGB observations and then re-encode them into policy inputs, introducing a…
RoboFL: Federated Expert Assembly for World Action Models
Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8
Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heter…
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…
DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation
Yunfan Lou, Hewen Gao, Xiyu Zhu +6
Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on the current frame and execute…
FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation
Shuyi Zhang, Yunfan Lou, Hongyang Cheng +8
Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data. While Reinforcement Learning (RL) fine-tuning can surpass this limit…
Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
Jiajun Li, Tiecheng Guo, Yifan Ye +10
World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, many existing WAMs rely o…
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…
HCSG: Human-Centric Semantic-Geometric Reasoning for Vision-Language Navigation
Haoxuan Xu, Tianfu Li, Wenbo Chen +7
VLN has achieved remarkable progress by scaling data and model capacity. However, the assumption of a static environment breaks down in real-world indoor scenarios, where robots in…
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9
World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However, standard approaches often fo…
ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation
Zixuan Liu, Ruoyi Qiao, Chenrui Tie +5
Reinforcement learning explores effectively in domains such as Atari games, navigation, and locomotion, where novelty over states or dynamics is a sufficient signal. In contrast, d…