arXiv:2602.02762v2 Announce Type: replace Abstract: Semi-supervised imitation learning (SSIL) consists in learning a policy from a small dataset of action-labeled trajectories and a much larger dataset of action-free trajectories. Some SSIL methods learn an inverse dynamics model (IDM) to predict the action from the current state and the next state. An IDM can act as a policy when paired with a video model (VM-IDM) or as a label generator to perform behavior cloning on action-free data (IDM labeling). In this work, we first show that VM-IDM and IDM labeling learn the same policy in a limit cas
This research addresses a fundamental challenge in AI development (data scarcity for supervised learning) and offers a method to improve model training efficiency, becoming more critical as data demands for advanced AI systems grow.
Improved sample efficiency in imitation learning can accelerate the development of complex AI behaviors in environments where labeled data is expensive or difficult to obtain, fostering advancements in autonomous systems.
The ability to learn effectively from smaller labeled datasets combined with larger unlabeled datasets reduces the data bottleneck for certain AI applications, potentially lowering development costs and accelerating deployment.
- · AI developers
- · Robotics companies
- · Autonomous systems sector
- · Research institutions
More efficient training of AI models for tasks requiring imitation learning.
Faster iteration and deployment of AI agents in real-world scenarios, particularly in robotics and control systems.
Reduced computational and data infrastructure requirements for developing advanced AI capabilities, democratizing access to complex AI development.
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