# Interactive Robotic Grasping with Attribute-Guided Disambiguation

arXiv: https://arxiv.org/abs/2203.08037 · HTML: https://mlsanity.com/paper/2203.08037/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2022.

## Built on by (most cited first, 19 of 19)

- [A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter](https://mlsanity.com/paper/2302.12610/) (2023)
- [CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents](https://mlsanity.com/paper/2306.10376/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model](https://mlsanity.com/paper/2308.15962/) (2023)
- [Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models](https://mlsanity.com/paper/2408.07975/) (2024)
- [Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting](https://mlsanity.com/paper/2512.20014/) (2025)
- [DoRO: Disambiguation of referred object for embodied agents](https://mlsanity.com/paper/2207.14205/) (2022)
- [LLMs for Robotic Object Disambiguation](https://mlsanity.com/paper/2401.03388/) (2024)
- [GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning](https://mlsanity.com/paper/2409.14403/) (2024)
- [PROGrasp: Pragmatic Human-Robot Communication for Object Grasping](https://mlsanity.com/paper/2309.07759/) (2023)
- [InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions](https://mlsanity.com/paper/2310.12147/) (2023)
- [SInViG: A Self-Evolving Interactive Visual Agent for Human-Robot Interaction](https://mlsanity.com/paper/2402.11792/) (2024)
- [Language-driven Grasp Detection with Mask-guided Attention](https://mlsanity.com/paper/2407.19877/) (2024)
- [Naming Objects for Vision-and-Language Manipulation](https://mlsanity.com/paper/2303.02871/) (2023)
- [Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation](https://mlsanity.com/paper/2512.21065/) (2025)
- [LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation](https://mlsanity.com/paper/2511.02239/) (2025)
- [StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning](https://mlsanity.com/paper/2605.03927/) (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)

## Abstract (arXiv)

Interactive robotic grasping using natural language is one of the most fundamental tasks in human-robot interaction. However, language can be a source of ambiguity, particularly when there are ambiguous visual or linguistic contents. This paper investigates the use of object attributes in disambiguation and develops an interactive grasping system capable of effectively resolving ambiguities via dialogues. Our approach first predicts target scores and attribute scores through vision-and-language grounding. To handle ambiguous objects and commands, we propose an attribute-guided formulation of the partially observable Markov decision process (Attr-POMDP) for disambiguation. The Attr-POMDP utilizes target and attribute scores as the observation model to calculate the expected return of an attribute-based (e.g., "what is the color of the target, red or green?") or a pointing-based (e.g., "do you mean this one?") question. Our disambiguation module runs in real time on a real robot, and the interactive grasping system achieves a 91.43% selection accuracy in the real-robot experiments, outperforming several baselines by large margins.