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Yilei Zhang

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NeurIPS Conference 2025 Conference Paper

Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models

  • Daoyuan Chen
  • Yilun Huang
  • Xuchen Pan
  • Jiang Nana
  • Haibin Wang
  • Yilei Zhang
  • Ce Ge
  • Yushuo Chen

Foundation models demand advanced data processing for their vast, multimodal datasets. However, traditional frameworks struggle with the unique complexities of multimodal data. In response, we present Data-Juicer 2. 0, a data processing system backed by 100+ data processing operators spanning text, image, video, and audio modalities, supporting more critical tasks including data analysis, synthesis, annotation, and foundation model post-training. With seamless compatibility and dedicated optimization for popular dataset hubs like Hugging Face and computing engines like Ray, it improves upon its predecessor in terms of usability, efficiency, and programmability. It features an easily accessible user interface layer that supports decoupled Python interactions, RESTful APIs, and conversational commands. Its new runtime layer offers adaptive execution across diverse scales and environments, abstracting away system complexities. Extensive empirical evaluations demonstrate Data-Juicer 2. 0's remarkable performance and scalability, highlighting its capability to efficiently process TB-level data with 10k+ CPU cores. The system is publicly available and has been widely adopted in diverse research fields and real-world products such as Alibaba Cloud PAI. We actively maintain the system and share practical insights to foster research and applications of next-generation foundation models.

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IROS Conference 2016 Conference Paper

Active tactile object exploration with Gaussian processes

  • Zhengkun Yi
  • Roberto Calandra
  • Filipe Veiga
  • Herke van Hoof
  • Tucker Hermans
  • Yilei Zhang
  • Jan Peters 0001

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.

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