20 papers
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,…
FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution
Peize Li, Ruimeng Zhang, Ru Zhang +3
Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real…
Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation
Peng Ren, Haoyang Ge, Jiang Zhao +4
Vision-language-action policies are a promising foundation for general robot control, but long-horizon humanoid loco-manipulation requires the robot to treat task objects as persis…
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…
EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
Haoyang Ge, Peng Ren, Yukun Shi +3
Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified trajectories, and humanoid vision-l…
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…
EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
Zhaoyang Yang, Yurun Jin, Lizhe Qi +2
Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models. While recent world-action models joi…
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…
STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation
Yuxuan Tian, Yurun Jin, Bin Yu +5
Robotic manipulation requires reasoning about future spatial-temporal interactions and geometric constraints, yet existing Vision-Language-Action (VLA) policies often leave predict…
DanceCrafter: Fine-Grained Text-Driven Controllable Dance Generation via Choreographic Syntax
Hang Yuan, Xiaolin Hu, Yan Wan +8
Text-driven controllable dance generation remains under-explored, primarily due to the severe scarcity of high-quality datasets and the inherent difficulty of articulating complex…
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…
Cybo-Waiter: A Physical Agentic Framework for Humanoid Whole-Body Locomotion-Manipulation
Peng Ren, Haoyang Ge, Chuan Qi +5
Robots are increasingly expected to execute open ended natural language requests in human environments, which demands reliable long horizon execution under partial observability. T…
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…
A-LLM: An End-to-end Conversational Audio Avatar Large Language Model
Xiaolin Hu, Hang Yuan, Xinzhu Sang +4
Developing expressive and responsive conversational digital humans is a cornerstone of next-generation human-computer interaction. While large language models (LLMs) have significa…
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…