Summary of Reducing Human-robot Goal State Divergence with Environment Design, by Kelsey Sikes et al.
Reducing Human-Robot Goal State Divergence with Environment Design
by Kelsey Sikes, Sarah Keren, Sarath Sreedharan
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This paper addresses the challenge of aligning robot behavior with human expectations in human-AI collaborations. The authors propose a new metric called Goal State Divergence (GSD), which measures the difference between a robot’s final goal state and the one expected by a human user. The GSD value can be directly calculated or approximated using maximal and minimal bounds. A novel design problem, Human-Robot Goal Alignment (HRGA), is then introduced to identify environment modifications that prevent mismatches. The effectiveness of GSD in reducing differences between human-robot goal states is empirically evaluated on several standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix a big problem in working with robots and humans together. Sometimes, the robot doesn’t understand what we want it to do, which can lead to bad outcomes. To solve this, the authors created a new way to measure how far apart our goals are from the robot’s goals. They also developed a plan to find ways to make the environment better so that the robot and human are on the same page. This helps reduce mistakes and makes it safer for everyone. |
Keywords
» Artificial intelligence » Alignment
