6 papers
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…
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
Qiang Li, Yingwenqi Jiang, Tuoxi Li +17
Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large vi…
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…
Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
Chuyang Xiao, Dawei Wang, Xinzheng Tang +2
This paper presents a mixed traffic control policy designed to optimize traffic efficiency across diverse road topologies, addressing issues of congestion prevalent in urban enviro…