Simple Kinesthetic Haptics for Object Recognition

TL;DR

This paper introduces a kinesthetic-based object recognition method using grasp observations from multi-fingered robotic hands, eliminating the need for tactile sensors and extensive real-world data collection, and demonstrating accurate classification with minimal grasps.

Contribution

The paper presents a novel, sensor-independent approach for object recognition that relies solely on grasp kinematics and computationally generated training data, enabling scalable and adaptable robotic perception.

Findings

Accurate object classification with few grasps

Method scalable to various object sizes

Effective recognition of general geometries

Abstract

Object recognition is an essential capability when performing various tasks. Humans naturally use either or both visual and tactile perception to extract object class and properties. Typical approaches for robots, however, require complex visual systems or multiple high-density tactile sensors which can be highly expensive. In addition, they usually require actual collection of a large dataset from real objects through direct interaction. In this paper, we propose a kinesthetic-based object recognition method that can be performed with any multi-fingered robotic hand in which the kinematics is known. The method does not require tactile sensors and is based on observing grasps of the objects. We utilize a unique and frame invariant parameterization of grasps to learn instances of object shapes. To train a classifier, training data is generated rapidly and solely in a computational process without interaction with real objects. We then propose and compare between two iterative algorithms that can integrate any trained classifier. The classifiers and algorithms are independent of any particular robot hand and, therefore, can be exerted on various ones. We show in experiments, that with few grasps, the algorithms acquire accurate classification. Furthermore, we show that the object recognition approach is scalable to objects of various sizes. Similarly, a global classifier is trained to identify general geometries (e.g., an ellipsoid or a box) rather than particular ones and demonstrated on a large set of objects. Full scale experiments and analysis are provided to show the performance of the method.