Hyperbot-A Benchmarking Testbed For Acquisition Of Robot-Centric Hyperspectral Scene And In-Hand Object Data

Hanson, Nathaniel; Kelestemur, Tarik; Berman, Joseph; Ritzenhoff, Dominik; Padır, Taşkın

doi:10.1109/whispers56178.2022.9955133

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Sensory gaps in material perception are especially apparent where the deformation of surfaces might cause a failed grasp, or stuck vehicle. In many works, including several not included in the scope of this dissertation [200,201,199], we explored the use of spectroscopy to make an impact in many challenging robot perception problems. We successfully deployed systems that can estimate material class and regress physical quantities in the real world.…”

Section: Discussionmentioning

confidence: 99%

“…Our work shows that forward-facing HSI is a powerful tool for robotics and is useful in a variety of terrain conditions. Part of this work originally appeared in [157]. The contributions of this chapter are as follows.…”

Section: Architecture and Calibrationmentioning

confidence: 99%

“…The output fiber optic cable for the in-hand sensing is coupled to a bifurcated fiber optic cable (Ocean Insight) which directs the signal from the output into a VNIR spectrometer (Pebble VIS-NIR, Ibsen Photonics) and NIR spectrometer (Pebble NIR, Ibsen Photonics). This spectrometer pairing was used in [157], which discusses how the spectral signals from the two devices are combined into a unified spectral signal, The curvature sensing fiber is coupled into a separate VNIR spectrometer (Blue Wave VIS, StellarNet). Regulated air pressure at 200 kPa is supplied to a fluidic control board (Soft Robotics Toolkit) to control actuation of the finger.…”

Section: Optical Ground Truthmentioning

confidence: 99%

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Material informed robotics - spectral perception for object identification and parameter inference

Hanson

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Section: Discussionmentioning

confidence: 99%

Section: Architecture and Calibrationmentioning

confidence: 99%

Section: Optical Ground Truthmentioning

confidence: 99%

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Material informed robotics - spectral perception for object identification and parameter inference

Hanson

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Occluded object detection and exposure in cluttered environments with automated hyperspectral anomaly detection

2022

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Cluttered environments with partial object occlusions pose significant challenges to robot manipulation. In settings composed of one dominant object type and various undesirable contaminants, occlusions make it difficult to both recognize and isolate undesirable objects. Spatial features alone are not always sufficiently distinct to reliably identify anomalies under multiple layers of clutter, with only a fractional part of the object exposed. We create a multi-modal data representation of cluttered object scenes pairing depth data with a registered hyperspectral data cube. Hyperspectral imaging provides pixel-wise Visible Near-Infrared (VNIR) reflectance spectral curves which are invariant in similar material types. Spectral reflectance data is grounded in the chemical-physical properties of an object, making spectral curves an excellent modality to differentiate inter-class material types. Our approach proposes a new automated method to perform hyperspectral anomaly detection in cluttered workspaces with the goal of improving robot manipulation. We first assume the dominance of a single material class, and coarsely identify the dominant, non-anomalous class. Next these labels are used to train an unsupervised autoencoder to identify anomalous pixels through reconstruction error. To tie our anomaly detection to robot actions, we then apply a set of heuristically-evaluated motion primitives to perturb and further expose local areas containing anomalies. The utility of this approach is demonstrated in numerous cluttered environments including organic and inorganic materials. In each of our four constructed scenarios, our proposed anomaly detection method is able to consistently increase the exposed surface area of anomalies. Our work advances robot perception for cluttered environments by incorporating multi-modal anomaly detection aided by hyperspectral sensing into detecting fractional object presence without need for laboriously curated labels.

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Hyperbot-A Benchmarking Testbed For Acquisition Of Robot-Centric Hyperspectral Scene And In-Hand Object Data

Cited by 2 publications

References 15 publications

Material informed robotics - spectral perception for object identification and parameter inference

Material informed robotics - spectral perception for object identification and parameter inference

Occluded object detection and exposure in cluttered environments with automated hyperspectral anomaly detection

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