arXiv:2509.15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections. However, scaling this paradigm requires addressing two key challenges. First, human motion data acquired from videos, motion capture systems, or generative models often contains spatial noise, jitter, and frame-level flickering, which can be amplified during retargeting and lead to unsafe or physically infeasible robot motions. Second, existing motion re
This paper represents a significant step in addressing critical technical challenges (data quality and retargeting fidelity) that have hindered the scalable deployment of humanoid robots.
The ability to efficiently transfer human motion to humanoid robots is crucial for unlocking their potential in various industries, moving them from research labs to practical applications.
The development of scalable and robust motion transfer methods mitigates key bottlenecks in training and operating humanoid robots, accelerating their commercial viability.
- · Humanoid robotics manufacturers
- · AI/robotics research institutions
- · Logistics and manufacturing sectors
- · AI data providers
- · Labor-intensive industries without automation adoption
- · Companies reliant on bespoke, manual robot programming
More efficient and cost-effective development of humanoid robot applications.
Increased investment and acceleration in the commercialization of humanoid robots across multiple sectors.
Potential for humanoid robots to displace a significant portion of manual labor in specific industries sooner than previously anticipated.
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Read at arXiv cs.AI