8 papers
RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models
Bin Yu, Yao Zhang, Haishan Liu +9
Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction.…
FrameSkip: Learning from Fewer but More Informative Frames in VLA Training
Bin Yu, Shijie Lian, Xiaopeng Lin +8
Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as i…
3D-Mix for VLA: A Plug-and-Play Module for Integrating VGGT-based 3D Information into Vision-Language-Action Models
Bin Yu, Shijie Lian, Xiaopeng Lin +8
Vision-Language-Action (VLA) models leverage Multimodal Large Language Models (MLLMs) for robotic control, but recent studies reveal that MLLMs exhibit limited spatial intelligence…
TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers
Bin Yu, Shijie Lian, Xiaopeng Lin +8
The fundamental premise of Vision-Language-Action (VLA) models is to harness the extensive general capabilities of pre-trained Vision-Language Models (VLMs) for generalized embodie…
TrajSelector: Harnessing Latent Representations for Efficient and Effective Best-of-N in Large Reasoning Model
Bin Yu, Xinming Wang, Shijie Lian +6
Large language models (LLMs) have shown remarkable progress in complex reasoning tasks, largely enabled by test-time scaling (TTS) paradigms that allocate additional compute during…
Deep Sparse Latent Feature Models for Knowledge Graph Completion
Haotian Li, Rui Zhang, Lingzhi Wang +6
Recent advances in knowledge graph completion (KGC) have emphasized text-based approaches to navigate the inherent complexities of large-scale knowledge graphs (KGs). While these m…