5 papers
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…
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…
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,…