1 citations · 1 across the 7 of their papers we have counts for
7 papers
AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation
Chenyang Li, Kaige Li, Zeyu Jiang +1
Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances. Most existing methods rely on white-box access to targ…
REAL: Robust Extreme Agility via Spatio-Temporal Policy Learning and Physics-Guided Filtering
Jialong Liu, Dehan Shen, Yanbo Wen +2
Extreme legged parkour demands rapid terrain assessment and precise foot placement under highly dynamic conditions. While recent learning-based systems achieve impressive agility,…
SaferPath: Hierarchical Visual Navigation with Learned Guidance and Safety-Constrained Control
Lingjie Zhang, Zeyu Jiang, Changhao Chen
Visual navigation is a core capability for mobile robots, yet end-to-end learning-based methods often struggle with generalization and safety in unseen, cluttered, or narrow enviro…
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
Ke Zhang, Lixin Xu, Chengyi Song +4
Dexterous manipulation remains one of the most challenging problems in robotics, requiring coherent control of high-DoF hands and arms under complex, contact-rich dynamics. A major…
VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
Chongkai Gao, Zixuan Liu, Zhenghao Chi +8
Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action gener…
DexSinGrasp: Learning a Unified Policy for Dexterous Object Singulation and Grasping in Densely Cluttered Environments
Lixin Xu, Zixuan Liu, Zhewei Gui +6
Grasping objects in cluttered environments remains a fundamental yet challenging problem in robotic manipulation. While prior works have explored learning-based synergies between p…
Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning
Zeyu Jiang, Hai Huang, Xingquan Zuo
Deep reinforcement learning (DRL) has successfully addressed many complex control problems. However, the neural networks representing policies or values remain opaque, undermining…