FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens

Yiming Zhong, Yumeng Liu, Chuyang Xiao, Zemin Yang, Youzhuo Wang, Yufei Zhu, Yujing Sun, Xinge Zhu, Yuexin Ma†

This paper proposes FreqPolicy, a frequency-domain autoregressive visuomotor policy that progressively models hierarchical frequency components with continuous latent representations, achieving superior accuracy and efficiency in robotic manipulation tasks.

DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover

Youzhuo Wang*, Jiayi Ye*, Chuyang Xiao, Yiming Zhong, Heng Tao, Hang Yu, Yumeng Liu, Jingyi Yu, Yuexin Ma†

This paper introduces DexH2R, a real-world dataset for human-to-robot handovers featuring dexterous motions, diverse objects, and rich annotations. Using teleoperation, it captures natural human-like behaviors for robotic learning. We also propose DynamicGrasp, a handover solution, and benchmark state-of-the-art methods, providing key insights for future work.

Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark

Chuyang Xiao*, Daiwei Wang*, Xinzheng Tang, Jia Pan, Yuexin Ma†

This paper presents three main contributions to mixed traffic control: (1) the first large-scale real-world benchmark with 444 scenarios from 20 countries; (2) a model-free RL method using Robot Vehicles to optimize traffic flow; (3) superior performance demonstrated in intersections and roundabouts. The benchmark enables realistic policy evaluation in diverse environments.

T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation

Pengliang Ji*, Chuyang Xiao*, Huilin Tai, Mingxiao Huo

This work introduces T2VBench, the first comprehensive benchmark for evaluating text-to-video models' temporal dynamics. Key contributions include: (1) a hierarchical framework with 1,600+ temporal prompts and 5,000 human-rated videos across 16 dimensions; (2) systematic evaluation of leading models (ZeroScope, Pika) revealing their temporal capabilities and limitations; (3) insights for improving future T2V metrics. The benchmark enables rigorous temporal analysis in video generation.

Carnegie Mellon University

August 2025 - August 2027 (Expected)

Major: Master of Science in Robotics

ShanghaiTech University

September 2021 - June 2025

Major: B.E. in Computer Science

University of California, Berkeley

August 2023 - May 2024

Major: Computer Science (Exchange student in EECS Department)