11 papers · 1 filter
GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
AgiBot Research Team, Renhang Liu, Wenzhi Zhao +42
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video…
KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
Yaping Li, Zhaxizhuoma, Qiaojun Yu +3
Articulated object manipulation requires an understanding of kinematic structure that is difficult and costly to learn from robot demonstrations alone. We introduce the Kinematic-A…
RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation
Ziqin Wang, Hao Li, Weijun Wang +5
Existing robot datasets remain expensive to curate, embodiment-specific, and insufficiently annotated with the fine-grained structure required for generalizable reasoning, executio…
Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration
Xiaoye Zhu, Weixin Li, Junan Huo +5
A fundamental intent asymmetry plagues modern 3D asset creation: while state-of-the-art 3D toolchains demand precise, executable parameters, ordinary users typically provide vague,…
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In pract…
ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation
Yang Li, Zhaxizhuoma, Hongru Jiang +11
Embodied intelligence for contact-rich manipulation has predominantly relied on position control, while explicit awareness and regulation of interaction forces remain under-explore…
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…
InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
Junhao Cai, Zetao Cai, Jiafei Cao +39
Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, b…
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…
Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction
Sizhe Yang, Linning Xu, Hao Li +4
3D spatial perception is fundamental to generalizable robotic manipulation, yet obtaining reliable, high-quality 3D geometry remains challenging. Depth sensors suffer from noise an…
RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation
Boyang Wang, Haoran Zhang, Shujie Zhang +8
The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting la…