ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer
Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered. Although recent approaches have attempted to mitigate this issue via zero-shot transfer, they are often constrained by predefined, discrete class systems, limiting their adaptability to novel or compositional task variations. We propose a significantly more generalized approach, replacing discrete latent variables with natural language conditioning via a text-conditioned Variational Autoencoder (VAE). Our core innovation utilizes a Large Language Model (LLM) as a dynamic \textit{semantic operator} at test time. Rather than relying on rigid rul...
| Authors | Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana |
|---|---|
| Primary category | cs.AI |
| Published | 9 April 2026 |
| Last updated at source | 10 April 2026 |
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