Summary of Groot-2: Weakly Supervised Multi-modal Instruction Following Agents, by Shaofei Cai et al.
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents
by Shaofei Cai, Bowei Zhang, Zihao Wang, Haowei Lin, Xiaojian Ma, Anji Liu, Yitao Liang
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 Developing robots that can follow complex instructions across multiple formats, such as text and images, remains a significant challenge in robotics and artificial intelligence. While large-scale pre-training on unlabeled datasets has enabled agents to learn diverse behaviors, these agents often struggle with following specific instructions. To address this issue, researchers framed the problem as a semi-supervised learning task and introduced GROOT-2, a multimodal instructable agent trained using a novel approach that combines weak supervision with latent variable models. The method consists of two key components: constrained self-imitating, which utilizes large amounts of unlabeled demonstrations to enable the policy to learn diverse behaviors, and human intention alignment, which uses a smaller set of labeled demonstrations to ensure the latent space reflects human intentions. GROOT-2’s effectiveness is validated across four diverse environments, ranging from video games to robotic manipulation, demonstrating its robust multimodal instruction-following capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine robots that can follow instructions not just in one way, but in many ways – like text, images, and even videos. Researchers want to make this happen, so they created a new robot called GROOT-2. This robot uses a special method that combines lots of practice with some guidance from humans. The goal is for the robot to learn how to follow instructions in different situations, like playing video games or moving objects around. By testing GROOT-2 in many different environments, scientists found that it can indeed follow complex instructions and make good decisions. |
Keywords
» Artificial intelligence » Alignment » Latent space » Semi supervised
