6 papers
Feeling the Unexpected: ResTacVLA for Contact-Rich Manipulation via Residual Tactile Representation
Pengwei Zhang, Bin Xie, Ce Hao +5
Tactile perception is indispensable for contact-rich manipulation, yet integrating it into Vision-Language-Action (VLA) models often induces modality collapse, where high-bandwidth…
A Low-Cost Vision-Based Tactile Gripper with Pretraining Learning for Contact-Rich Manipulation
Yaohua Liu, Binkai Ou, Zicheng Qiu +2
Robotic manipulation in contact-rich environments remains challenging, particularly when relying on conventional tactile sensors that suffer from limited sensing range, reliability…
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…
Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
Ce Hao, Xuanran Zhai, Yaohua Liu +1
Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size an…
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…
VFP: Variational Flow-Matching Policy for Multi-Modal Robot Manipulation
Xuanran Zhai, Qianyou Zhao, Qiaojun Yu +1
Flow-matching-based policies have recently emerged as a promising approach for learning-based robot manipulation, offering significant acceleration in action sampling compared to d…