Summary of Introspective Planning: Aligning Robots’ Uncertainty with Inherent Task Ambiguity, by Kaiqu Liang et al.
Introspective Planning: Aligning Robots’ Uncertainty with Inherent Task Ambiguity
by Kaiqu Liang, Zixu Zhang, Jaime Fernández Fisac
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 Large language models (LLMs) have shown impressive reasoning abilities, allowing robots to understand natural language instructions and plan complex actions. However, LLMs can hallucinate, leading to robots executing plans that are misaligned with user goals or even unsafe in critical situations. To address this issue, we propose introspective planning, a systematic approach that aligns the LLM’s uncertainty with the inherent ambiguity of the task. Our method constructs a knowledge base containing examples of introspective reasoning as post-hoc rationalizations of human-selected safe and compliant plans, which are retrieved during deployment. In our evaluations on three tasks, including a newly introduced safe mobile manipulation benchmark, we demonstrate that introspection substantially improves both compliance and safety over state-of-the-art LLM-based planning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) can help robots understand natural language instructions and make good decisions. However, these models can sometimes make mistakes and do things that are not what the user wants or even unsafe. To fix this problem, scientists came up with a new way to plan called introspective planning. This approach helps the LLM think more carefully about its choices and make sure they are safe and correct. The team tested their method on three different tasks and found that it worked much better than other ways of using LLMs for planning. |
Keywords
* Artificial intelligence * Knowledge base
