Author

Ye Shi

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Preprint Oct 2026

From Social Reasoning to Embodied Interaction: An Agentic Framework for Social Robots

Natural face-to-face human--robot interaction requires a robot to understand an evolving social situation, decide when to engage, and express its intent through coordinated physical behavior. Yet existing approaches rarely close this loop: foundation-model agents provide increasingly capable multimodal reasoning and me...

Zi-Yu Cheng, Yue-Wen Guo, Zhi-Rui Liu et al. · 0 citations

#machine learning Preprint Oct 2026

CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance

Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their ability to represent multimodal action distributions offers rich opportunities for exploration in reinforcement learning (RL). However, optimizing diffusion policies with RL c...

Shu-Tong Ding, Zhao-Yang Liu, Yi-Miao Zhou et al. · 0 citations

Preprint Oct 2026

FASTER: Fast Adjoint Stochastic Transport for Endpoint Refinement in Reward-Guided Image Editing

Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing approaches optimize candidates through pretrained generation processes, making repeated adjustment depend on costly large-model execution and, in some cases, backbone backp...

Yi-Miao Zhou, Ze-Jia Zhong, Jing-Ya Wang et al. · 0 citations

Preprint Sep 2026

EmbodiRSI: Recursive Self-Improvement for Data-Efficient Robot Adaptation

Adapting robot manipulation policies to new tasks and environments remains highly data-intensive, while the data needed for further improvement depends on the policy's current capabilities and failure modes. We introduce EmbodiRSI, an agentic system for recursive self-improvement (RSI) in a real-to-sim-to-real setting,...

Hao-Ran Lang, Hao-Tao Lu, Shi-Yu Sang et al. · 3 citations

#machine learning Preprint Sep 2026

QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning

This work proposes QAMM, a method that turns the critic-derived adjoint signal into supervision for MeanFlow's average velocity, and derives the adjoint MeanFlow target, specify its gradient boundaries, and train it with an offline actor-critic.

Yu Gong, Shu-Tong Ding, Mo-Kai Pan et al. · 0 citations

Preprint Aug 2026

In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion

In-Context Forcing is introduced, a progressive autoregressive paradigm that utilizes contexts with decreasing noise levels that enables cross-frame parallel denoising, achieving substantial inference acceleration without sacrificing performance.

Lingxiao Yang, Liu Liu, Mo-Ran Li et al. · 2 citations

Preprint Jul 2026

TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation

TactiDex is introduced, a real-world tactile-guided benchmark specifically designed to move dexterous manipulation beyond kinematic mimicry toward contact-level human-likeness and TactiSkill, a framework built upon a novel tri-component tactile reward that innovatively uses tactile signals as structured supervision.

Suting Ni, Hanbing Zhang, Zhenyu Wei et al. · 3 citations