Robotic Task Ambiguity Resolution via Natural Language Interaction (2504.17748v1)

Abstract: 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.

Robotic Task Ambiguity Resolution via Natural Language Interaction

The paper "Robotic Task Ambiguity Resolution via Natural Language Interaction," authored by Eugenio Chisari, Jan Ole von Hartz, Fabien Despinoy, and Abhinav Valada, introduces AmbResVLM, an innovative approach to enhancing the reliability and accuracy of language-conditioned robotic policies by resolving ambiguities inherent in task descriptions. The core contribution of the work lies in its ability to preemptively identify and disambiguate tasks where traditional models might falter due to unclear language specifications.

AmbResVLM leverages Vision-LLMs (VLMs) to interpret both visual and linguistic data, facilitating an automated reasoning process about task descriptions within the context of the observed scene. By grounding language goals in visual observations, AmbResVLM proactively queries users for clarification of ambiguous tasks, thus resolving potential misinterpretations at the outset. The structured process involves grounding task-relevant objects, classifying task ambiguity, generating user queries, and resolving ambiguities through user interaction. Such clarity ensures the enhanced success of downstream robotic policies.

One of the striking numerical results highlighted is the substantial improvement in the average success rate of robotic tasks—from 69.6% to 97.1%—when integrating AmbResVLM for ambiguity resolution. This performance metric emphasizes the effectiveness of AmbResVLM in practical applications, allowing robots to accurately understand and execute tasks based on unambiguous, clarified commands.

Comparative evaluations against KnowNo, a state-of-the-art baseline, indicate that AmbResVLM competently achieves task ambiguity resolution despite relying on image-based data without privileged object information. In simulation and real-world scenarios, AmbResVLM demonstrates robust performance in grounding task objects and interpreting user clarifications with high accuracy.

From the perspective of broader implications, the approach outlined in this paper holds promise for more sophisticated human-robot interactions, particularly in dynamic and unstructured environments where language-based commands may inherently be unclear. By refining how robots interpret task instructions through natural language, we advance the potential for more autonomous and adaptive robotics systems in practical settings. Furthermore, the success of AmbResVLM suggests possibilities for future developments in vision-language-action models (VLAs), which can incorporate reasoning capabilities to further augment robotic decision-making processes.

To conclude, the integration of AmbResVLM in language-conditioned policies underscores significant advancements in addressing task ambiguity—an area previously underexplored. As such, this research represents a critical step in refining how we train and deploy robotic systems to interpret and act upon natural language commands with improved precision and reliability. As foundation models continue to evolve, there is substantial scope for expanding upon these methods to enhance generalization, contextual understanding, and human-robot interactive modalities.