arXiv:2509.26294v2 Announce Type: replace Abstract: We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretraining or high-capacity architectures can be difficult to apply, and efficiency with respect to demonstration data becomes critical. We introduce Noise-Guided Transport (NGT), a lightweight off-policy method that casts imitation as an optimal transport problem solved via adversarial training. NGT requires no pretraining or specialized architectures, incorporates uncer
The increasing focus on AI efficiency and data constraints in practical applications drives research into robust learning methods like Noise-Guided Transport.
This research addresses a critical limitation in AI deployment by enabling effective imitation learning with limited data, making advanced AI more accessible and efficient for specialized tasks.
Imitation learning methods can now perform effectively in low-data environments without extensive pretraining or complex architectures, broadening the applicability of AI.
- · AI developers
- · Robotics companies
- · Industries with scarce expert data
- · Researchers in reinforcement learning
- · AI models requiring large datasets
- · Expensive data collection services
More efficient and cost-effective AI model development in data-scarce domains becomes possible.
Accelerated deployment of AI agents in niche industrial and operational settings where large datasets are impractical.
Reduced barriers to entry for AI development, potentially diversifying the global AI ecosystem beyond regions with massive data infrastructure.
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Read at arXiv cs.LG