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Arxiv
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59
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Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement
- Imitation Learning from Observation (IfO) enables large-scale behavior learning by using action-free demonstrations.
- Current IfO research typically focuses on idealized scenarios with limited data distributions.
- This paper introduces a method to learn from more nuanced data distributions, aiming for iterative self-improvement in imitation learning.
- The study adapts RL-based imitation learning to action-free demonstrations with a value function and highlights the importance of more practical IfO techniques for scalable behavior learning.
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