Attribute-Based Robotic Grasping with One-Grasp Adaptation

TL;DR

This paper presents an end-to-end attribute-based robotic grasping method that enables quick adaptation to novel objects with only one grasp attempt, achieving high success rates in simulation and real-world tests.

Contribution

It introduces a novel fusion of visual and textual attributes with one-grasp adaptation, improving grasping performance on unknown objects.

Findings

Achieves over 80% grasp success rate on unseen objects.

Outperforms baseline methods significantly.

Generalizes from simulation to real-world scenarios.

Abstract

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in clutter remains challenging. This paper attempts to tackle the challenge by leveraging object attributes that facilitate recognition, grasping, and quick adaptation. In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances. Besides, we utilize object persistence before and after grasping to learn a joint metric space of visual and textual attributes. Our model is self-supervised in a simulation that only uses basic objects of various colors and shapes but generalizes to novel objects and real-world scenes. We further demonstrate that our model is capable of adapting to novel objects with only one grasp data and improving instance grasping performance significantly. Experimental results in both simulation and the real world demonstrate that our approach achieves over 80\% instance grasping success rate on unknown objects, which outperforms several baselines by large margins.