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Robotics

GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors

Tianyi Xie, Haotian Zhang

Featured June 7, 2026

Original Paper

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Simply

GRAIL creates realistic robot training data by first building a virtual 3D world and then using AI to generate human movements within that specific, known environment.

In depth
The paper introduces GRAIL, a pipeline that synthesizes humanoid loco-manipulation data by leveraging 3D asset-conditioned generation and interaction-aware optimization. By specifying the 3D scene geometry and camera parameters before video generation, the authors bypass the ambiguities of in-the-wild video reconstruction, enabling the creation of physically plausible, robot-compatible trajectories for task-general tracking.

Key Takeaways

  • 1
    Asset-conditioned generation allows for the synthesis of over 20,000 physically plausible loco-manipulation sequences without physical teleoperation.
  • 2
    Interaction-aware optimization anchors 4D human-object interaction trajectories to known metric scales, significantly improving physical executability.
  • 3
    Task-general tracking policies enable the transfer of generated data to real-world humanoid robots, achieving high success rates in pick-up and stair-climbing.

Conceptual Flow

HIGH LEVEL
1
Methodology

The system builds a virtual world first, then uses AI to create human movements that fit perfectly into that world.

3D Assets
Video AI
Generate and Refine
Robot Motion Data
2
Results

The generated data is used to train robots to perform complex tasks in the real world.

Generated Data
Train Robot
Real World Success