Summary of Goal Inference From Open-ended Dialog, by Rachel Ma et al.
Goal Inference from Open-Ended Dialog
by Rachel Ma, Jingyi Qu, Andreea Bobu, Dylan Hadfield-Menell
First submitted to arxiv on: 17 Oct 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 This paper introduces an online method for embodied agents, such as robots or virtual assistants, to learn and accomplish diverse user goals. The approach is efficient in terms of data requirements, unlike offline methods like Reinforcement Learning with Human Feedback (RLHF). Instead, the authors extract natural language goal representations from conversations with Large Language Models (LLMs) and use these to prompt an LLM to role-play as a human with different goals. This allows for Bayesian inference over potential goals, enabling the representation of uncertainty over complex goals based on unrestricted dialog. The method is evaluated in two domains: grocery shopping using a text-based interface and home robot assistance using AI2Thor simulation. Results show that the proposed method outperforms ablation baselines that lack either explicit goal representation or probabilistic inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots and virtual assistants understand what people want to do, like buying groceries or helping around the house. The authors came up with a new way for these agents to learn from conversations with people, without needing huge amounts of data. They used special language models that can chat like humans, and then asked them to pretend they were different people trying to accomplish different tasks. This allowed the robots to figure out what people might want to do based on what they said, even if it was a complicated goal. The authors tested their method in two situations: buying groceries online and helping with household chores using a virtual robot. Their results showed that this new approach worked better than older methods that didn’t use natural language or probability. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Probability » Prompt » Reinforcement learning » Rlhf
