Proposes an online Bayesian inference method for embodied AI agents to learn user goals from open‑ended dialog
Uses LLM prompts to role‑play different user goals and derives likelihoods for Bayesian updating
Emphasizes maintaining uncertainty about inferred goals to avoid uncertain actions
Evaluated in a text‑based grocery shopping domain and AI2Thor robot simulation
Shows comparable flexibility to offline RLHF while being more sample‑efficient
Contains ablation studies highlighting the importance of goal representation and probabilistic inference
📖 Full Retelling
WHO: Rachel Ma, Jingyi Qu, Andreea Bobu, and Dylan Hadfield‑Menell are the authors of the paper. WHAT: They present an online approach for embodied AI agents to infer and represent user goals from open‑ended dialog by prompting Large Language Models to role‑play and applying Bayesian inference to maintain uncertainty over natural‑language goal representations. WHERE: The method is evaluated in two environments—a text‑based grocery shopping domain and the AI2Thor robotic simulation. WHEN: The preprint was first submitted on 17 October 2024 (v1) and revised on 19 February 2026 (v2). WHY: The authors aim to give embodied agents the ability to efficiently learn diverse user goals without relying on large offline datasets, thereby improving robustness and user alignment.
The paper argues that because humans naturally communicate preferences in natural language, embodied agents should extract goals directly from dialog in the same format. It also contends that agents must maintain and quantify uncertainty about inferred goals so they act only when highly confident. The authors build an online pipeline wherein a Large Language Model is prompted to role‑play a human with various possible goals. The likelihoods of these role‑plays are used to perform Bayesian inference over potential goals, resulting in a probabilistic representation of uncertain objective functions. The proposed method is contrasted with ablation baselines that omit explicit goal representation or probabilistic inference. Experimental results in the grocery and AI2Thor settings demonstrate that the approach achieves competitive flexibility with online efficiency compared to offline RLHF techniques.
🏷️ Themes
Embodied AI, Goal inference, Large Language Models, Bayesian inference, Online learning, Uncertainty quantification
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Deep Analysis
Why It Matters
The paper introduces an online method for embodied AI agents to infer user goals from natural language dialogue, enabling more efficient and robust task assistance. By quantifying uncertainty through Bayesian inference, agents can act confidently on well-understood goals.
Context & Background
Embodied AI agents increasingly interact with humans via open-ended dialogue
Traditional offline methods like RLHF require large datasets and are less efficient
The authors propose using LLMs to role-play humans and extract goal representations for online inference
What Happens Next
Future work may extend the approach to more complex, real-world domains and integrate multimodal inputs. Researchers might also explore tighter coupling between the inference module and the agent’s control policy.
Frequently Asked Questions
What is the main contribution of the paper?
An online Bayesian inference framework that extracts natural language goal representations from dialogue using LLM role-play, allowing embodied agents to quantify uncertainty and act on well-understood goals.
How does the method differ from offline RLHF?
Unlike RLHF, which trains on large static datasets, the proposed method learns goals in real time from conversation, reducing data requirements and enabling faster adaptation.
In which environments was the method evaluated?
The authors tested it in a text-based grocery shopping scenario and in the AI2Thor robot simulation, comparing against baselines that lack explicit goal representation or probabilistic inference.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2410.13957 [Submitted on 17 Oct 2024 ( v1 ), last revised 19 Feb 2026 (this version, v2)] Title: Goal Inference from Open-Ended Dialog Authors: Rachel Ma , Jingyi Qu , Andreea Bobu , Dylan Hadfield-Menell View a PDF of the paper titled Goal Inference from Open-Ended Dialog, by Rachel Ma and 3 other authors View PDF HTML Abstract: Embodied AI Agents are quickly becoming important and common tools in society. These embodied agents should be able to learn about and accomplish a wide range of user goals and preferences efficiently and robustly. Large Language Models are often used as they allow for opportunities for rich and open-ended dialog type interaction between the human and agent to accomplish tasks according to human preferences. In this thesis, we argue that for embodied agents that deal with open-ended dialog during task assistance: 1) AI Agents should extract goals from conversations in the form of Natural Language to be better at capturing human preferences as it is intuitive for humans to communicate their preferences on tasks to agents through natural language. 2) AI Agents should quantify/maintain uncertainty about these goals to ensure that actions are being taken according to goals that the agent is extremely certain about. We present an online method for embodied agents to learn and accomplish diverse user goals. While offline methods like RLHF can represent various goals but require large datasets, our approach achieves similar flexibility with online efficiency. We extract natural language goal representations from conversations with Large Language Models . We prompt an LLM to role play as a human with different goals and use the corresponding likelihoods to run Bayesian inference over potential goals. As a result, our method can represent uncertainty over complex goals based on unrestricted dialog. We evaluate in a text-based grocery shopping domain and an AI2Thor robot simulation. We compare our ...
