# Robotic Task Ambiguity Resolution via Natural Language Interaction

arXiv: https://arxiv.org/abs/2504.17748 · HTML: https://mlsanity.com/paper/2504.17748/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2025.

## Built on by (most cited first, 8 of 8)

- [Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition](https://mlsanity.com/paper/2511.14391/) (2025)
- [Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate](https://mlsanity.com/paper/2507.12370/) (2025)
- [Preference-Based Long-Horizon Robotic Stacking with Multimodal Large Language Models](https://mlsanity.com/paper/2509.24163/) (2025)
- [Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search](https://mlsanity.com/paper/2603.05642/) (2026)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)
- [ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries](https://mlsanity.com/paper/2605.06223/) (2026)
- [Active Perception for Embodied Disambiguation](https://mlsanity.com/paper/2608.13605/) (2026)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)

## Abstract (arXiv)

Language-conditioned policies have recently gained substantial adoption in robotics as they allow users to specify tasks using natural language, making them highly versatile. While much research has focused on improving the action prediction of language-conditioned policies, reasoning about task descriptions has been largely overlooked. Ambiguous task descriptions often lead to downstream policy failures due to misinterpretation by the robotic agent. To address this challenge, we introduce AmbResVLM, a novel method that grounds language goals in the observed scene and explicitly reasons about task ambiguity. We extensively evaluate its effectiveness in both simulated and real-world domains, demonstrating superior task ambiguity detection and resolution compared to recent state-of-the-art baselines. Finally, real robot experiments show that our model improves the performance of downstream robot policies, increasing the average success rate from 69.6% to 97.1%. We make the data, code, and trained models publicly available at https://ambres.cs.uni-freiburg.de.