Embodied AI 101

Shaoqing Tan
  • 5,0 (1)
  • Технологии
  • Ежедневно

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  1. -12 ч

    Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding

    Learns dexterous manipulation policies that explicitly ground actions in generative contact predictions from visuotactile observations, improving robustness on contact-rich tasks.

    27 мин.

  2. -21 ч

    Freeform Preference Learning (FPL) for Robotic Manipulation

    Introduces multi-axis preference supervision to learn dense, language-conditioned rewards across speed/precision/subtask axes without segmentation; enables compositional generalization and better long-horizon credit assignment than single-reward baselines.

    49 мин.

  3. -1 дн.

    Orca: The World is in Your Mind

    Proposes a general world foundation model leveraging Next-State-Prediction to jointly generate text, images, and embodied actions within a unified framework.

    29 мин.

  4. -1 дн.

    Qwen-RobotNav: A Scalable Unified Navigation Model for Agentic Robotics

    A unified 2B–8B parameter model for robot navigation tasks (VLN, ObjectNav, tracking, autonomous driving) via a configurable observation protocol, with demonstrated zero-shot deployment on real quadruped robots using agentic planners.

    37 мин.

  5. -5 дн.

    Scaling Robot Skills from Cheap Human Videos

    Replaces noisy 6-DoF hand poses with relative wrist translation as a shared action space between humans and bimanual robots, enabling scalable skill acquisition from inexpensive video data. This approach outperforms full-pose baselines for robot skill learning.

    15 мин.

  6. -5 дн.

    ABC: An Open Behavior Cloning Stack for Bimanual Manipulation

    Large-scale open-source framework for real-world robotic manipulation using behavior cloning, including the ABC-130K dataset with 3,500 hours and 130K+ episodes across 195 tasks. Provides hardware setups, simulators, and training recipes for Diffusion Transformers and Vision-Language-Action models.

    33 мин.

  7. -5 дн.

    Qwen-RobotManip: Alignment Unlocks Scale for Robotic Manipulation Foundation Models

    Presents alignment techniques to scale robotic manipulation foundation models, building on the Qwen model family for dexterous robot control.

    20 мин.

  8. -6 дн.

    ASPIRE: Automated Skill Discovery for Robotics

    Introduces the first automated system that continuously discovers, evolves, and accumulates reusable sensorimotor skills via evolutionary search over control programs, enabling compounding multi-task, sim-to-real, and cross-embodiment transfer without retraining end-to-end policies.

    31 мин.
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Информация

  • Автор
    Shaoqing Tan
  • Годы выхода
    2025 - 2026
  • Выпуски
    167
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © 2026 Shaoqing Tan

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