11 papers
ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching
Shuoheng Zhang, Yifu Yuan, Hongyao Tang +7
Existing imitation learning methods enable robots to interact autonomously with the physical environment. However, contact-rich manipulation tasks remain a significant challenge du…
SPAGS: Sparse-View Articulated Object Reconstruction from Single State via Planar Gaussian Splatting
Di Wu, Liu Liu, Xueyu Yuan +5
Articulated objects are ubiquitous in daily environments, and their 3D reconstruction holds great significance across various fields. However, existing articulated object reconstru…
CoFreeVLA: Collision-Free Dual-Arm Manipulation via Vision-Language-Action Model and Risk Estimation
Xuanran Zhai, Binkai Ou, Qiaojun Yu +2
Vision Language Action (VLA) models enable instruction following manipulation, yet dualarm deployment remains unsafe due to under modeled selfcollisions between arms and grasped ob…
REArtGS: Reconstructing and Generating Articulated Objects via 3D Gaussian Splatting with Geometric and Motion Constraints
Di Wu, Liu Liu, Zhou Linli +5
Articulated objects, as prevalent entities in human life, their 3D representations play crucial roles across various applications. However, achieving both high-fidelity textured su…
Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation
Qianyou Zhao, Yuliang Shen, Xuanran Zhai +5
In visuomotor policy learning, diffusion-based imitation learning has become widely adopted for its ability to capture diverse behaviors. However, approaches built on ordinary and…
ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
Jiawen Yu, Hairuo Liu, Qiaojun Yu +9
Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with c…