arXiv:2606.03017v1 Announce Type: new Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factorized Contrastive Abstractions for Transferable IRL (ConTraIRL), a framework that enables compositional reward transfer by learning decoupled latent representations of these two factors. ConTraIRL uses a dual-encoder architecture that maps observations into separate dynamics and goal latent spaces, trained with a dual contrastive objective. Temporal alignment encourages
The paper addresses a critical challenge in IRL, which is the unreliability of reward transfer when generalizing policies to novel combinations of environment dynamics and task goals, highlighting a current bottleneck in AI development.
This development significantly advances the capability of AI systems to learn and transfer complex behaviors, enabling more robust and versatile autonomous agents, which is crucial for real-world deployment.
AI systems can now better generalize learned behaviors to novel environments and tasks by decoupling and recomposing their understanding of dynamics and goals, moving beyond rigid, task-specific training.
- · AI agents developers
- · Robotics industry
- · Logistics and automation companies
- · Companies reliant on highly specialized, non-transferable AI models
AI agents can more efficiently adapt to new operational conditions with less retraining.
Accelerated development and deployment of autonomous systems in complex, dynamic environments.
Enhanced AI capabilities could lead to broader integration of autonomous systems across various economic sectors, potentially creating new industries or significantly transforming existing ones faster than anticipated.
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