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Robots learn force control from generated audio and video

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method to improve robotic manipulation by integrating audio generation with video generation. This approach addresses the limitations of purely kinematic trajectories, which often fail in tasks requiring precise force control. By using the loudness of generated contact sounds to shape a desired force profile, the system derives both motion trajectories and force parameters from natural language prompts. This force-aware system has been successfully demonstrated on a Franka Panda robot, enabling manipulation in contact-rich scenarios where previous methods failed, and also serves as a data generation engine for training new policies. AI

IMPACT This research could lead to more robust and versatile robotic manipulation capabilities, especially in complex, contact-dependent tasks.

RANK_REASON The cluster contains an academic paper detailing a new research methodology for robotics.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Robots learn force control from generated audio and video

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The cluster contains an academic paper detailing a new research methodology for robotics.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Guanhua Ji, Tianyu Li, Dayoon Suh, Yuqian Zhang, Boyan Zhang, Nadia Figueroa ·

    Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation

    arXiv:2609.19137v1 Announce Type: cross Abstract: Recent advances in video generation allow robots to learn manipulation trajectories from generated videos. However, these approaches produce purely kinematic trajectories that lack force information, causing failures in contact-ri…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation

    Recent advances in video generation allow robots to learn manipulation trajectories from generated videos. However, these approaches produce purely kinematic trajectories that lack force information, causing failures in contact-rich tasks where appropriate contact forces are esse…

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