5 citations · 8 across the 9 of their papers we have counts for
10 papers
Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution
Weichen Xu, Zhenhua Liu, Lin Luo +8
Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.e., execution horizon) before replanning, turning replanning into a task-agnostic peri…
TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning
ZhiYuan Feng, Yu Deng, Ruichuan An +11
In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean task specification. Such re…
HiSpatial: Taming Hierarchical 3D Spatial Understanding in Vision-Language Models
Huizhi Liang, Yichao Shen, Yu Deng +5
Achieving human-like spatial intelligence for vision-language models (VLMs) requires inferring 3D structures from 2D observations, recognizing object properties and relations in 3D…
MobileManiBench: Simplifying Model Verification for Mobile Manipulation
Wenbo Wang, Fangyun Wei, QiXiu Li +5
Vision-language-action models have advanced robotic manipulation but remain constrained by reliance on the large, teleoperation-collected datasets dominated by the static, tabletop…
VideoVLA: Video Generators Can Be Generalizable Robot Manipulators
Yichao Shen, Fangyun Wei, Zhiying Du +5
Generalization in robot manipulation is essential for deploying robots in open-world environments and advancing toward artificial general intelligence. While recent Vision-Language…
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies
Zhiying Du, Bei Liu, Yaobo Liang +7
Generalist vision--language--action (VLA) policies are typically trained on heterogeneous mixtures of robot demonstrations spanning diverse embodiments, action spaces, and observat…
Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos
Qixiu Li, Yu Deng, Yaobo Liang +14
This paper presents a novel approach for pretraining robotic manipulation Vision-Language-Action (VLA) models using a large corpus of unscripted real-life video recordings of human…
Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes
Zhiyuan Feng, Zhaolu Kang, Qijie Wang +16
Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent V…
UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping
Wenbo Wang, Fangyun Wei, Lei Zhou +9
We introduce UniGraspTransformer, a universal Transformer-based network for dexterous robotic grasping that simplifies training while enhancing scalability and performance. Unlike…
CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
Qixiu Li, Yaobo Liang, Zeyu Wang +15
The advancement of large Vision-Language-Action (VLA) models has significantly improved robotic manipulation in terms of language-guided task execution and generalization to unseen…