ELITE: Experiential Learning and Intent-Aware Transfer for Self-improving Embodied Agents
Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks. VLMs can learn rich semantic knowledge from static data but lack the ability to interact with the world. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from their own environment interaction experiences, and transfer acquir...
| Authors | Bingqing Wei, Zhongyu Xia, Dingai Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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