Classification of Household Materials via Spectroscopy

@article{Erickson2018ClassificationOH,
  title={Classification of Household Materials via Spectroscopy},
  author={Zackory M. Erickson and Nathan Luskey and S. Chernova and Charles C. Kemp},
  journal={IEEE Robotics and Automation Letters},
  year={2018},
  volume={4},
  pages={700-707}
}
Recognizing an object's material can inform a robot on the object's fragility or appropriate use. To estimate an object's material during manipulation, many prior works have explored the use of haptic sensing. In this letter, we explore a technique for robots to estimate the materials of objects using spectroscopy. We demonstrate that spectrometers provide several benefits for material recognition, including fast response times and accurate measurements with low noise. Furthermore… 
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29 Citations
Background Citations
15
Methods Citations
11
Results Citations
2

29 Citations

Pregrasp Object Material Classification by a Novel Gripper Design with Integrated Spectroscopy

It is concluded that spectroscopy is a promising sensing modality for enabling robots to not only classify grasped objects but also understand their underlying material composition.

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This work is addressing a problem of haptic material classification by using novel Deep Neural Network architecture which is able to deal with long signal sequences and achieving a 97.96 % classification accuracy.

Object Description Using Visual and Tactile Data

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Study of Reflection-Lass-Based Material Identification from Common Building Surfaces

Compared to existing material identification methods, the proposed reflection-loss-based method is capable of identifying materials from a significant distance without requiring any contact with the object and without requiring dedicated sensors from the infrastructure point of view.

The Visual Segmentation of Scene Information and Applications in Predictive Haptics

This work develops a way to segment a scene for the various objects in the scene using a pre-trained neural network that takes in spectroscopy measurements and pictures of a small patch of each object so that the PR2 can infer interactions with these objects.

Deeply Supervised Subspace Learning for Cross-Modal Material Perception of Known and Unknown Objects

A depth-supervised subspace cross-modal material retrieval model is trained to learn a common low-dimensional feature representation to capture the clustering structure among different modal features of the same class of objects.

A Framework for Sensorimotor Cross-Perception and Cross-Behavior Knowledge Transfer for Object Categorization

The results indicate that sensorimotor knowledge about objects can be transferred both across behaviors and across sensory modalities, such that a new robot can bootstrap its category recognition models without having to exhaustively explore the full set of objects.

Design of an Object Scanning System and a Calibration Method for a Fingertip-Mounted Dual-Modal and Dual Sensing Mechanisms (DMDSM)-based Pretouch Sensor for Grasping

The system design and ranging accuracy have been verified by physical experiments, and the collected data from seven types of common household objects have shown promising prospects of using DMDSM sensors in grasping.

Friction from Reflectance: Transfer Learning Approach

  • Piotr KickiK. Walas
  • Computer Science
    2019 4th International Conference on Robotics and Automation Engineering (ICRAE)
  • 2019
This paper is proposing a new approach of estimating friction coefficient from vision, i.e. reflectance images, based on transfer learning as well as the use of pre-trained networks to solve the friction estimation task.

Programmable Spectrometry: Per-pixel Material Classification using Learned Spectral Filters

This paper proposes the concept of programmable spectrometry for per-pixel material classification, where instead of sensing the HSI of the scene and then processing it, this is achieved using a computational camera with a programmable spectral response.

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