10 papers
Uni-PNP: Unified Prehensile and Non-prehensile Policy Learning for Generalizable Object Pose Reaching
Wenxuan Zhu, Yuze Wang, Jiangran Lyu +6
Object manipulation often requires combining non-prehensile actions with grasping. However, existing approaches typically develop these capabilities separately and integrate them b…
What Visual Representations Do World Action Models Need?
Kai Liu, Jiangran Lyu, Tingyu Deng +9
World Action Models (WAMs) inherit powerful priors from pretrained video generation models, but their variational autoencoder (VAE) representations are optimized for visual reconst…
Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies
Galbot Team, Xuchuan Chen, Xiaoqian Cheng +31
GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabilities as general-purpose embo…
GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning
Long Xu, Zhiqi Zhang, Mi Yan +8
Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment for both. Automated scene ge…
ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation
Mi Yan, Wenhao Zhang, Zhiqi Zhang +14
Zero-shot generalization to unseen embodiments is important for generalizable vision-language-action (VLA) models as robot hardware evolves and task-specific data collection remain…
KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
Yuansen Huang, Jiayi Chen, Haoran Liu +6
Generating high-quality dexterous grasps remains challenging for learning-based methods, which often depend on carefully tuned contact losses or costly contact-based test-time refi…
Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning
Yixin Zheng, Jiangran Lyu, Yifan Zhang +8
Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity in cluttered scenes remains challengin…
Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds
Xianzhe Fan, Shengliang Deng, Xiaoyang Wu +7
Existing Vision-Language-Action (VLA) models typically take 2D images as visual input, which limits their spatial understanding in complex scenes. How can we incorporate 3D informa…
StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
Shengliang Deng, Mi Yan, Yixin Zheng +7
While Vision-Language-Action (VLA) models excel in generalist manipulation, they often lack fine-grained spatial awareness and show limited viewpoint robustness. This limitation la…
GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data
Shengliang Deng, Mi Yan, Songlin Wei +10
Embodied foundation models are gaining increasing attention for their zero-shot generalization, scalability, and adaptability to new tasks through few-shot post-training. However,…