1 citations · 1 across the 9 of their papers we have counts for
10 papers
Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control
Jun Chen, Erdent Bao, Erdemt Bao +8
Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. However, generalizing to multi-step…
RoboRouter: Training-Free Policy Routing for Robotic Manipulation
Yiteng Chen, Zhe Cao, Hongjia Ren +9
Research on robotic manipulation has developed a diverse set of policy paradigms, including vision-language-action (VLA) models, vision-action (VA) policies, and code-based composi…
GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
Zihao Guo, Qingyun Sun, Ziwei Zhang +4
Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL app…
Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop
Tianxing Chen, Kaixuan Wang, Zhaohui Yang +96
Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical…
FrankenBot: Brain-Morphic Modular Orchestration for Robotic Manipulation with Vision-Language Models
Shiyi Wang, Wenbo Li, Yiteng Chen +2
Developing a general robot manipulation system capable of performing a wide range of tasks in complex, dynamic, and unstructured real-world environments has long been a challenging…
T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models
Yiteng Chen, Wenbo Li, Shiyi Wang +2
Building a general robotic manipulation system capable of performing a wide variety of tasks in real-world settings is a challenging task. Vision-Language Models (VLMs) have demons…
AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation
Wenbo Li, Shiyi Wang, Wenhao Li +3
Natural-language instructions specify manipulation goals but leave the robot's motion underdetermined. We present AntiGrounding, a visual action-selection framework built around a…
Analytic Task Scheduler: Recursive Least Squares Based Method for Continual Learning in Embodied Foundation Models
Lipei Xie, Yingxin Li, Huiping Zhuang
Embodied foundation models are crucial for Artificial Intelligence (AI) interacting with the physical world by integrating multi-modal inputs, such as proprioception, vision and la…
WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
Sihan Chen, Dan Zhao, Jongwoo Ko +5
The growing computational demands of large language models (LLMs) make efficient inference and activation strategies increasingly critical. While recent approaches, such as Mixture…
Grasp What You Want: Embodied Dexterous Grasping System Driven by Your Voice
Junliang Li, Kai Ye, Haolan Kang +6
In recent years, as robotics has advanced, human-robot collaboration has gained increasing importance. However, current robots struggle to fully and accurately interpret human inte…