15 papers · 1 filter
VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness
Hang Li, Mingxin Zhang, Zihan Wu +9
Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera view…
LA4VLA: Learning to Act without Seeing via Language-Action Pretraining
Tao Lin, Yuxin Du, Yiran Mao +13
Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visu…
AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding
Qize Yu, Jiadi You, Yuran Wang +12
Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the…
SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds
Yunsong Zhou, Hangxu Liu, Xuekun Jiang +12
Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the varia…
UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data
Sizhe Yang, Yiman Xie, Zhixuan Liang +4
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the…
RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation
Hao Li, Ziqin Wang, Zi-han Ding +9
Advances in large vision-language models (VLMs) have stimulated growing interest in vision-language-action (VLA) systems for robot manipulation. However, existing manipulation data…
Nimbus: A Unified Embodied Synthetic Data Generation Framework
Zeyu He, Yuchang Zhang, Yuanzhen Zhou +10
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acqui…
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…
InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation
Shuai Yang, Hao Li, Bin Wang +7
To operate effectively in the real world, robots should integrate multimodal reasoning with precise action generation. However, existing vision-language-action (VLA) models often s…
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…
ReSim: Generating High-Fidelity Simulation Data via 3D-Photorealistic Real-to-Sim for Robotic Manipulation
Xiaoshen Han, Junqiu Yu, Minghuan Liu +6
Real-world data collection for robotics is costly and resource-intensive, requiring skilled operators and expensive hardware. Simulations offer a scalable alternative but often fai…
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…