arXiv:2606.03201v1 Announce Type: cross Abstract: Reinforcement learning from expert videos across visually distinct domains is challenging due to the absence of reward signals and the presence of domain gaps. We introduce XIPER (Cross-domain Video Prediction Reward), a reward model for learning from expert videos collected in a visually different domain, where the agent's appearance differs due to factors such as color, morphology, or the sim-to-real gap. More specifically, XIPER trains a cross-domain video prediction model that maps agent observations into the expert domain and uses the pred
The proliferation of various simulation and real-world environments for AI training necessitates robust methods for cross-domain learning to maximize data utility.
This development addresses a fundamental challenge in applying reinforcement learning, enabling agents to learn from diverse datasets despite visual discrepancies between training and operational environments, thus accelerating AI deployment.
AI agents can now more effectively leverage disparate video data sources for learning, reducing the need for costly and time-consuming domain-specific data collection and annotation.
- · AI agents developers
- · Robotics companies
- · Simulation software providers
- · Defense autonomous systems
- · Companies reliant on single-domain data
- · Traditional manual data labeling services
Improved performance and faster deployment of AI systems trained on cross-domain expert videos.
Reduced barriers to entry for developing AI applications by lowering data collection and preparation costs.
Acceleration of autonomous system development across various industries, from manufacturing to logistics and defense.
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Read at arXiv cs.AI