# Robot Object Retrieval with Contextual Natural Language Queries

arXiv: https://arxiv.org/abs/2006.13253 · HTML: https://mlsanity.com/paper/2006.13253/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 26 of 26)

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- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
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- [Grounding Language with Visual Affordances over Unstructured Data](https://mlsanity.com/paper/2210.01911/) (2022)
- [Instruction-driven history-aware policies for robotic manipulations](https://mlsanity.com/paper/2209.04899/) (2022)
- [A Joint Network for Grasp Detection Conditioned on Natural Language Commands](https://mlsanity.com/paper/2104.00492/) (2021)
- [Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions](https://mlsanity.com/paper/2205.04028/) (2022)
- [Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding](https://mlsanity.com/paper/2301.11564/) (2023)
- [CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection](https://mlsanity.com/paper/2410.07540/) (2024)
- [WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model](https://mlsanity.com/paper/2308.15962/) (2023)
- [Learning Visual-Audio Representations for Voice-Controlled Robots](https://mlsanity.com/paper/2109.02823/) (2021)
- [Pretraining on Interactions for Learning Grounded Affordance Representations](https://mlsanity.com/paper/2207.02272/) (2022)
- [I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches](https://mlsanity.com/paper/2205.04026/) (2022)
- [Goal Inference from Open-Ended Dialog](https://mlsanity.com/paper/2410.13957/) (2024)
- [Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching](https://mlsanity.com/paper/2402.17372/) (2024)
- [DM2RM: Dual-Mode Multimodal Ranking for Target Objects and Receptacles Based on Open-Vocabulary Instructions](https://mlsanity.com/paper/2408.07910/) (2024)
- [SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images](https://mlsanity.com/paper/2412.02140/) (2024)
- [PROGrasp: Pragmatic Human-Robot Communication for Object Grasping](https://mlsanity.com/paper/2309.07759/) (2023)
- [Neural Variational Learning for Grounded Language Acquisition](https://mlsanity.com/paper/2107.14593/) (2021)
- [Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech](https://mlsanity.com/paper/2112.13758/) (2021)
- [FArMARe: a Furniture-Aware Multi-task methodology for Recommending Apartments based on the user interests](https://mlsanity.com/paper/2309.03100/) (2023)
- [A Language-based solution to enable Metaverse Retrieval](https://mlsanity.com/paper/2312.14630/) (2023)
- [CRAFT: A Neuro-Symbolic Framework for Visual Functional Affordance Grounding](https://mlsanity.com/paper/2507.14426/) (2025)
- [PGA: Personalizing Grasping Agents with Single Human-Robot Interaction](https://mlsanity.com/paper/2310.12547/) (2023)
- [CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding](https://mlsanity.com/paper/2512.04231/) (2025)
- [FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition](https://mlsanity.com/paper/2608.12683/) (2026)

## Abstract (arXiv)

Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the desired object's type such as "scissors" and/or visual attributes such as "red," thus limiting the robot to only known object classes. We develop a model to retrieve objects based on descriptions of their usage. The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects and selects the object that best satisfies the task specified by the verb. Our model directly predicts an object's appearance from the object's use specified by a verb phrase. We do not need to explicitly specify an object's class label. Our approach allows us to predict high level concepts like an object's utility based on the language query. Based on contextual information present in the language commands, our model can generalize to unseen object classes and unknown nouns in the commands. Our model correctly selects objects out of sets of five candidates to fulfill natural language commands, and achieves an average accuracy of 62.3% on a held-out test set of unseen ImageNet object classes and 53.0% on unseen object classes and unknown nouns. Our model also achieves an average accuracy of 54.7% on unseen YCB object classes, which have a different image distribution from ImageNet objects. We demonstrate our model on a KUKA LBR iiwa robot arm, enabling the robot to retrieve objects based on natural language descriptions of their usage. We also present a new dataset of 655 verb-object pairs denoting object usage over 50 verbs and 216 object classes.