Summary of Building Open-ended Embodied Agent Via Language-policy Bidirectional Adaptation, by Shaopeng Zhai et al.
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation
by Shaopeng Zhai, Jie Wang, Tianyi Zhang, Fuxian Huang, Qi Zhang, Ming Zhou, Jing Hou, Yu Qiao, Yu Liu
First submitted to arxiv on: 12 Dec 2023
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 The paper presents a framework called OpenPAL for building embodied agents that can integrate large language models and reinforcement learning to plan decision-making for open-ended tasks. The current research faces challenges in meeting the requirement of open-endedness, as existing methods typically train language models to adapt to fixed counterparts, limiting exploration of novel skills and hindering human-AI interaction. To address this issue, OpenPAL consists of two stages: fine-tuning a pre-trained language model to translate human instructions into goals for planning, and goal-conditioned training a policy for decision-making. The framework is tested using Contra, an open-ended first-person shooter game, demonstrating that agents trained with OpenPAL can comprehend arbitrary instructions and execute tasks efficiently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a robot new skills. You want it to understand what you mean when you say “pick up the ball” or “make a sandwich.” But current methods have limitations – they only work well if you tell the robot exactly how to do something, like “go left and then right.” This limits the robot’s ability to learn new things on its own. The OpenPAL framework tries to solve this problem by combining two technologies: language models that understand human language, and reinforcement learning that lets robots learn from trying different actions. The results show that agents trained with OpenPAL can follow instructions and perform tasks efficiently. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Reinforcement learning
