Qianyou Zhao

Xuanran Zhai, Qianyou Zhao, Qiaojun Yu et al.

Flow-matching-based policies have recently emerged as a promising approach for learning-based robot manipulation, offering significant acceleration in action sampling compared to diffusion-based policies. However, conventional flow-matching methods struggle with multi-modality, often collapsing to averaged or ambiguous behaviors in complex manipulation tasks. To address this, we propose the Variational Flow-Matching Policy (VFP), which introduces a variational latent prior for mode-aware action generation and effectively captures both task-level and trajectory-level multi-modality. VFP further incorporates Kantorovich Optimal Transport (K-OT) for distribution-level alignment and utilizes a Mixture-of-Experts (MoE) decoder for mode specialization and efficient inference. We comprehensively evaluate VFP on 41 simulated tasks and 3 real-robot tasks, demonstrating its effectiveness and sampling efficiency in both simulated and real-world settings. Results show that VFP achieves a 49% relative improvement in task success rate over standard flow-based baselines in simulation, and further outperforms them on real-robot tasks, while still maintaining fast inference and a compact model size. More details are available on our project page: https://sites.google.com/view/varfp/

Qianyou Zhao, Wenqiao Li, Chiyu Wang et al.

High-fidelity teleoperation of dexterous robotic hands is essential for bringing robots into unstructured domestic environments. However, existing teleoperation systems often face a trade-off between performance and portability: vision-based capture systems are constrained by costs and line-of-sight requirements, while mechanical exoskeletons are bulky and physically restrictive. In this paper, we present DexEMG, a lightweight and cost-effective teleoperation system leveraging surface electromyography (sEMG) to bridge the gap between human intent and robotic execution. We first collect a synchronized dataset of sEMG signals and hand poses via a MoCap glove to train EMG2Pose, a neural network capable of continuously predicting hand kinematics directly from muscle activity. To ensure seamless control, we develop a robust hand retargeting algorithm that maps the predicted poses onto a multi-fingered dexterous hand in real-time. Experimental results demonstrate that DexEMG achieves high precision in diverse teleoperation tasks. Notably, our system exhibits strong generalization capabilities across novel objects and complex environments without the need for intensive individual-specific recalibration. This work offers a scalable and intuitive interface for both general-purpose robotic manipulation and assistive technologies.