19 papers
Relational Geometry Forcing for World Action Models
Yuan Xu, Yixiang Chen, Qisen Ma +10
World Action Models (WAMs) have shown strong potential for robot manipulation by jointly learning visual dynamics and executable actions. However, their video and action objectives…
What Matters for World Modeling in Vision-Language-Action Models?
Qisen Ma, Yixiang Chen, Yuan Xu +10
Integrating world models into Vision-Language-Action models (VLAs) has emerged as a promising direction, enabling policies to anticipate future dynamics beyond reactive observation…
XEWorld: Can Action-Conditioned World Models Generalize to Unseen Robot Embodiments?
Yixiang Chen, Jiabing Yang, Yuan Xu +10
Action-conditioned world models are promising learned simulators for robotic manipulation, yet evaluating them exclusively on training robots fails to reveal whether they capture p…
BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation
Peiyan Li, Yuze Zhu, Yixiang Chen +10
Leveraging pre-trained vision-language models (VLMs) to construct vision-language-action (VLA) models has emerged as a promising paradigm for 3D robot manipulation. However, existi…
Style over Substance: A Shortcut Audit of Emotion-Description Preference Evaluation
Jiabing Yang, Yixiang Chen, Yuan Xu +6
Preference over model-generated emotion descriptions is emerging as a standard evaluation metric for multimodal emotion understanding, exemplified by the MER2026 MER-Prefer track o…
FlowWAM: Optical Flow as a Unified Action Representation for World Action Models
Yixiang Chen, Peiyan Li, Yuan Xu +13
World Action Models (WAMs) are able to leverage pretrained video generators for both world modeling and action prediction. However, directly leveraging such video generators for co…
DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
Kai Wang, Zhaopeng Gu, Yixiang Chen +7
World-action models have shown promising robot-manipulation performance by jointly predicting future visual states and actions. However, existing methods mainly rely on short-term…
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
Wen Ye, Peiyan Li, Tingyu Yuan +7
Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve t…
Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision
Yuan Xu, Yixiang Chen, Kai Wang +5
Vision-Language-Action (VLA) models have shown strong potential for generalizable robotic manipulation. During fine-tuning, however, action supervision applies equally across all t…
SKIP: Sparse Keyframe Interpolation Paradigm for Efficient Embodied World Models
Ziheng He, Yixiang Chen, Ning Yang +11
Embodied world models have emerged as a promising paradigm in robotics by predicting how robot actions affect the surrounding scene. However, the rollout inference remains computat…
Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization
Yu Liu, Yihang Yin, Tianlv Huang +8
Assistive teleoperation enhances efficiency via shared control, yet inter-operator variability, stemming from diverse habits and expertise, induces highly heterogeneous trajectory…
SpatialVAM:Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy
Peiyan Li, Yixiang Chen, Yuan Xu +13
Robotic manipulation requires understanding both the 3D spatial structure of the environment and its temporal evolution, yet most existing policies neglect one or both aspects. The…
BridgeV2W: Bridging Video Generation Models to Embodied World Models via Embodiment Masks
Yixiang Chen, Peiyan Li, Jiabing Yang +8
Embodied world models have emerged as a promising paradigm in robotics, most of which leverage large-scale Internet videos or pretrained video generation models to enrich visual an…
AWARE: Action-Entropy-Weighted and Attention-Guided Retrieval of Observation Evidence for Vision-Language-Action Models
Jiabing Yang, Yixiang Chen, Yuan Xu +16
Vision-Language-Action (VLA) models combine multimodal perception with robotic control, demonstrating remarkable potential for generalizable manipulation. Yet, enhancing their exec…
EgoDemoGen: Egocentric Demonstration Generation for Viewpoint Generalization in Robotic Manipulation
Yuan Xu, Jiabing Yang, Xiaofeng Wang +16
Imitation learning based visuomotor policies have achieved strong performance in robotic manipulation, yet they often remain sensitive to egocentric viewpoint shifts. Unlike third-…
LaPA: Length-Aware Prefix and Prompt Attention Augmentation for Long-Form Controllable Text Generation
Jiabing Yang, Yixiang Chen, Zichen Wen +8
Prefix-based methods have emerged as a promising paradigm for Controllable Text Generation (CTG) due to their parameter efficiency. However, while effective in short sequences, the…
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2
Many real-world applications collect data in a streaming environment, where learning tasks are encountered sequentially. This necessitates continual learning (CL) to update models…
APR-Transformer: Initial Pose Estimation for Localization in Complex Environments through Absolute Pose Regression
Srinivas Ravuri, Yuan Xu, Martin Ludwig Zehetner +2
Precise initialization plays a critical role in the performance of localization algorithms, especially in the context of robotics, autonomous driving, and computer vision. Poor loc…
Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization
Yuanze Xu, Ming Dai, Wenxiao Cai +1
Image retrieval has been employed as a robust complementary technique to address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning. However, most existing methods p…