Summary of From Goal-conditioned to Language-conditioned Agents Via Vision-language Models, by Theo Cachet and Christopher R. Dance and Olivier Sigaud
From Goal-Conditioned to Language-Conditioned Agents via Vision-Language Models
by Theo Cachet, Christopher R. Dance, Olivier Sigaud
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to building language-conditioned agents (LCAs) using vision-language models (VLMs). The authors introduce a two-stage framework that first identifies an environment configuration with high VLM scores for text-based task descriptions, and then uses a goal-conditioned policy to reach that configuration. This decomposition enables the development of LCAs that can perform diverse tasks without requiring separate training for each task. The paper also explores enhancements such as distilled models and multi-view evaluation to improve the speed and quality of VLM-based LCAs. Experimental results demonstrate that this approach outperforms multi-task RL baselines in zero-shot generalization, showcasing its potential for enabling language-conditioned agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper talks about creating computer programs that can understand text and perform tasks based on what they read. The authors developed a new way to build these programs using special models that combine visual and language skills. Their approach involves finding the best environment for a task, then using a pre-trained policy to reach that environment. This allows the program to learn without needing separate training for each new task. The results show that their method is better than others at performing tasks without prior knowledge of those tasks. |
Keywords
» Artificial intelligence » Generalization » Multi task » Zero shot
