SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework
arXiv:2605.20373v1 Announce Type: cross Abstract: Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods either rely on laborious task-specific reward engineering, rigidly replay reference motions that fail to generalize, or depend on costly teleoperation that limits scalability. While human videos capture diverse human behaviors, motion priors inferred from them are inherently imperfect, suffering from occlusion, contact artifacts, and retargeting errors that render them unsuitable for direct policy le
The increased maturity of AI and robotics research is leading to new methods for training complex robotic systems, moving beyond traditional, labor-intensive approaches.
This research addresses fundamental challenges in developing highly capable and generalizable humanoid robots, critical for diverse real-world applications and economic impact.
The proposed method (SUGAR) offers a more scalable and robust way to train humanoid robots for complex loco-manipulation tasks by leveraging human video data more effectively.
- · Robotics research institutions
- · Humanoid robot manufacturers
- · Logistics and manufacturing sectors
- · AI software developers
- · Companies relying on manual labor for complex tasks
- · Traditional robotics training methodologies
- · Task-specific robot developers
More adaptable and commercially viable humanoid robots become available sooner.
Increased automation replaces some human jobs but creates new ones in robot maintenance and development.
Generalized humanoid robots become central to economic infrastructure, driving significant productivity gains and societal reconfigurations.
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Read at arXiv cs.AI