Summary of Do’s and Don’ts: Learning Desirable Skills with Instruction Videos, by Hyunseung Kim et al.
Do’s and Don’ts: Learning Desirable Skills with Instruction Videos
by Hyunseung Kim, Byungkun Lee, Hojoon Lee, Dongyoon Hwang, Donghu Kim, Jaegul Choo
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed DoDont algorithm, an instruction-based skill discovery method, addresses the challenges of learning complex behaviors and avoiding undesirable ones in unsupervised settings. The approach consists of two stages: instruction learning, where an instruction network is trained to distinguish desirable transitions from undesirable ones using action-free instruction videos; and skill learning, where the instruction network adjusts the reward function of the skill discovery algorithm to weight desired behaviors. By integrating the instruction network into a distance-maximizing skill discovery algorithm, DoDont effectively learns desirable behaviors and avoids undesirable ones across complex continuous control tasks. The algorithm’s performance is evaluated using less than 8 instruction videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The DoDont algorithm helps robots learn new skills without being told what to do or what not to do. It uses special videos that show the robot what actions are good or bad, and then adjusts its own rewards system to make sure it does the right things. This is important because current methods can cause the robot to do silly or even dangerous things. For example, a robot might learn how to stand up but struggle with walking or running. The DoDont algorithm can help solve this problem by teaching the robot what actions are good and what actions are bad. |
Keywords
» Artificial intelligence » Unsupervised
