9 papers
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
Zemin Yang, Yaoyu He, Yiming Zhong +5
Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control. While recent…
FastGrasp: Learning-based Whole-Body Control Method for Fast Dexterous Grasping with Mobile Manipulators
Heng Tao, Yiming Zhong, Zemin Yang +1
Fast dexterous grasping while a mobile base remains in motion requires coordinated whole-body control and rapid adaptation to physical contact. We propose FastGrasp, a two-stage le…
From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
Yiming Zhong, Yaoyu He, Zemin Yang +5
Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal sca…
VideoAfford: Grounding 3D Affordance from Human-Object-Interaction Videos via Multimodal Large Language Model
Hanqing Wang, Mingyu Liu, Xiaoyu Chen +9
3D affordance grounding aims to highlight the actionable regions on 3D objects, which is crucial for robotic manipulation. Previous research primarily focused on learning affordanc…
Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
Hanqing Wang, Shaoyang Wang, Yiming Zhong +7
Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It plays a vital role in the fields of hu…
DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover
Youzhuo Wang, Jiayi Ye, Chuyang Xiao +6
Handover between a human and a dexterous robotic hand is a fundamental yet challenging task in human-robot collaboration. It requires handling dynamic environments and a wide varie…
FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Yiming Zhong, Yumeng Liu, Chuyang Xiao +7
Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing metho…
EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment
Yufei Zhu, Yiming Zhong, Zemin Yang +4
Dexterous robotic hands often struggle to generalize effectively in complex environments due to the limitations of models trained on low-diversity data. However, the real world pre…
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness
Yiming Zhong, Qi Jiang, Jingyi Yu +1
A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexte…