# Open-Vocabulary Part-Based Grasping

arXiv: https://arxiv.org/abs/2406.05951 · HTML: https://mlsanity.com/paper/2406.05951/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 6 of 6)

- [Learning Precise Affordances from Egocentric Videos for Robotic Manipulation](https://mlsanity.com/paper/2408.10123/) (2024)
- [AffordGrasp: In-Context Affordance Reasoning for Open-Vocabulary Task-Oriented Grasping in Clutter](https://mlsanity.com/paper/2503.00778/) (2025)
- [GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation](https://mlsanity.com/paper/2508.14036/) (2025)
- [OpenTie: Open-vocabulary Sequential Rebar Tying System](https://mlsanity.com/paper/2509.00064/) (2025)
- [Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance](https://mlsanity.com/paper/2603.07866/) (2026)
- [DSAA: Dual-Stage Attribute Activation for Fine-grained Open Vocabulary Detection](https://mlsanity.com/paper/2605.18023/) (2026)

## Abstract (arXiv)

Many robotic tasks require grasping objects at specific object parts instead of arbitrarily, a crucial capability for interactions beyond simple pick-and-place, such as human-robot interaction, handovers, or tool use. Prior work has focused either on generic grasp prediction or task-conditioned grasping, but not on directly targeting object parts in an open-vocabulary way. We propose AnyPart, a modular framework that unifies open-vocabulary object detection, part segmentation, and 6-DoF grasp prediction to enable robots to grasp user-specified parts of arbitrary objects based on natural language prompts. We evaluate 16 model combinations, and demonstrate that the best-performing combination achieves 60.8% grasp success in cluttered real-world scenes at 60 times faster inference than existing approaches. To support this study, we introduce a new dataset for part-based grasping and conduct a detailed failure analysis. Our core insight is that modularly combining existing foundation models unlocks surprisingly strong and efficient capabilities for open-vocabulary part-based grasping without requiring additional training.