6 papers
InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy
Yang Tian, Yuyin Yang, Yiman Xie +13
Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of…
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
Xinyi Chen, Yilun Chen, Yanwei Fu +26
We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its…
GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation
Ning Gao, Yilun Chen, Shuai Yang +7
Robotic manipulation in real-world settings remains challenging, especially regarding robust generalization. Existing simulation platforms lack sufficient support for exploring how…
CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling
Hao Li, Shuai Yang, Yilun Chen +8
Recent vision-language-action (VLA) models built on pretrained vision-language models (VLMs) have demonstrated strong performance in robotic manipulation. However, these models rem…
Gripper Keypose and Object Pointflow as Interfaces for Bimanual Robotic Manipulation
Yuyin Yang, Zetao Cai, Yang Tian +2
Bimanual manipulation is a challenging yet crucial robotic capability, demanding precise spatial localization and versatile motion trajectories, which pose significant challenges t…
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Yang Tian, Sizhe Yang, Jia Zeng +4
Current efforts to learn scalable policies in robotic manipulation primarily fall into two categories: one focuses on "action," which involves behavior cloning from extensive colle…