GauTOAO: Gaussian-based Task-Oriented Affordance of Objects

TL;DR

GauTOAO is a Gaussian-based framework that enables robots to understand task-specific object affordances in real-time using vision-language models, improving manipulation accuracy.

Contribution

The paper introduces a novel zero-shot method combining vision-language models and Gaussian distributions for precise task-oriented object affordance detection.

Findings

Enhanced accuracy in affordance region prediction

Effective generalization across multiple tasks

Improved robot manipulation performance

Abstract

When your robot grasps an object using dexterous hands or grippers, it should understand the Task-Oriented Affordances of the Object(TOAO), as different tasks often require attention to specific parts of the object. To address this challenge, we propose GauTOAO, a Gaussian-based framework for Task-Oriented Affordance of Objects, which leverages vision-language models in a zero-shot manner to predict affordance-relevant regions of an object, given a natural language query. Our approach introduces a new paradigm: "static camera, moving object," allowing the robot to better observe and understand the object in hand during manipulation. GauTOAO addresses the limitations of existing methods, which often lack effective spatial grouping, by extracting a comprehensive 3D object mask using DINO features. This mask is then used to conditionally query gaussians, producing a refined semantic distribution over the object for the specified task. This approach results in more accurate TOAO extraction, enhancing the robot's understanding of the object and improving task performance. We validate the effectiveness of GauTOAO through real-world experiments, demonstrating its capability to generalize across various tasks.