6 citations · 11 across the 19 of their papers we have counts for
22 papers
EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents
Bin Yu, Shijie Lian, Zhaolong Shen +10
Spatial reasoning agents can inspect tool outputs and repair errors, but reusing these lessons requires preserving both the computation and the conditions that make it valid. We in…
PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models
DeepCybo Team, Yu Bin, Haipeng Cao +51
We present PhysBrain 1.5, a unified model for understanding physical environments, generating actions, and predicting future states. Motivated by the physical loop of observation,…
SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models
Changti Wu, Bin Yu, Zhaolong Shen +6
Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not necessarily yield better policie…
Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments
Xiaopeng Lin, Ruoqi Yang, Shijie Lian +14
Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributions, yet scaling such robot d…
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.…
PhysBrain 1.0 Technical Report
Shijie Lian, Bin Yu, Xiaopeng Lin +10
Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding. PhysBrain 1.0 studies a comple…
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…
IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
Shijie Lian, Bin Yu, Xiaopeng Lin +8
Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-hori…
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…
ScalSelect: Scalable Training-Free Multimodal Data Selection for Efficient Visual Instruction Tuning
Changti Wu, Jiahuai Mao, Yuzhuo Miao +6
Large-scale Visual Instruction Tuning (VIT) has become a key paradigm for advancing the performance of vision-language models (VLMs) across various multimodal tasks. However, train…
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…
LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries
Shijie Lian, Bin Yu, Xiaopeng Lin +6
Vision-Language-Action (VLA) models have shown promise in robot manipulation but often struggle to generalize to new instructions or complex multi-task scenarios. We identify a cri…
PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence
Xiaopeng Lin, Shijie Lian, Bin Yu +10
Robotic generalization relies on physical intelligence: the ability to reason about state changes, contact-rich interactions, and long-horizon planning under egocentric perception…
DynaSolidGeo: A Dynamic Benchmark for Genuine Spatial Mathematical Reasoning of VLMs in Solid Geometry
Changti Wu, Shijie Lian, Zihao Liu +3
Solid geometry problem solving demands spatial mathematical reasoning that integrates spatial intelligence and symbolic reasoning. However, most existing multimodal mathematical re…
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…
WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Runting Li, Shijie Lian, Hua Li +3
Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physica…
Euclid's Gift: Enhancing Spatial Perception and Reasoning in Vision-Language Models via Geometric Surrogate Tasks
Shijie Lian, Changti Wu, Laurence Tianruo Yang +4
Spatial intelligence spans a rich suite of abilities, including visualising and transforming shapes, mentally rotating objects, judging relational positions and containment, and es…
MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification
Dingkun Liu, Zhu Chen, Jingwei Luo +2
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. Recent EEG foundation models aim to learn generalized representations across di…
Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation
Hua Li, Shijie Lian, Zhiyuan Li +5
With recent breakthroughs in large-scale modeling, the Segment Anything Model (SAM) has demonstrated significant potential in a variety of visual applications. However, due to the…
TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian
Shijie Lian, Ziyi Zhang, Hua Li +4
Underwater 3D scene reconstruction is crucial for multimedia applications in adverse environments, such as underwater robotic perception and navigation. However, the complexity of…
Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Shijie Lian, Hua Li
With breakthroughs in large-scale modeling, the Segment Anything Model (SAM) and its extensions have been attempted for applications in various underwater visualization tasks in ma…
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
Shijie Lian, Ziyi Zhang, Hua Li +4
With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance…