3 citations · 3 across the 10 of their papers we have counts for
11 papers
Air-Ground Collaborative Vision-and-Language Navigation via Shared Bird's-Eye Maps
Shuning Zhang, Liang Li, Yunheng Wang +3
Air-ground collaborative Vision-and-Language Navigation (VLN) pairs an unmanned aerial vehicle (UAV) with a global bird's-eye view and an unmanned ground vehicle (UGV) with a local…
Not All Actions Are Equal: Rethinking Conditioning for Dexterous World Model
Zizhao Yuan, Zhengtu Liang, Taowen Wang +7
Recent advances in action-conditioned world models show promising progress in modeling complex interactions and forecasting future states under diverse action sequences. While thes…
V-Zero: Answer-Label-Free On-Policy Distillation with Contrastive Evidence Gating for Fine-Grained Visual Reasoning
Haoxiang Sun, Zhihang Yi, Langxuan Deng +6
Fine-grained visual reasoning requires multimodal large language models (MLLMs) to identify task-relevant visual evidence and ground their reasoning in local image regions. Existin…
Morphology-Consistent Humanoid Interaction through Robot-Centric Video Synthesis
Weisheng Xu, Jian Li, Yi Gu +12
Equipping humanoid robots with versatile interaction skills typically requires either extensive policy training or explicit human-to-robot motion retargeting. However, learning-bas…
Shifting Uncertainty to Critical Moments: Towards Reliable Uncertainty Quantification for VLA Model
Yanchuan Tang, Taowen Wang, Yuefei Chen +3
Vision-Language-Action (VLA) models enable general-purpose robotic policies by mapping visual observations and language instructions to low-level actions, but they often lack relia…
T2MBench: A Benchmark for Out-of-Distribution Text-to-Motion Generation
Bin Yang, Rong Ou, Weisheng Xu +7
Most existing evaluations of text-to-motion generation focus on in-distribution textual inputs and a limited set of evaluation criteria, which restricts their ability to systematic…
On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning
Changyu Liu, Yiyang Liu, Taowen Wang +7
Vision-Language-Action models have recently emerged as a powerful paradigm for general-purpose robot learning, enabling agents to map visual observations and natural-language instr…
DreamNav: A Trajectory-Based Imaginative Framework for Zero-Shot Vision-and-Language Navigation
Yunheng Wang, Yuetong Fang, Taowen Wang +6
Vision-and-Language Navigation in Continuous Environments (VLN-CE), which links language instructions to perception and control in the real world, is a core capability of embodied…
Target-driven Attack for Large Language Models
Chong Zhang, Mingyu Jin, Dong Shu +3
Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions…
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
Taowen Wang, Cheng Han, James Chenhao Liang +6
Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic i…
MPT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Taowen Wang, Yiyang Liu, James Chenhao Liang +11
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization cap…