Co-Learning of Task and Sensor Placement for Soft Robotics

Unlike rigid robots which operate with compact degrees of freedom, soft robots must reason about an infinite dimensional state space. Mapping this continuum state space presents significant challenges, especially when working with a finite set of discrete sensors. Reconstructing the robot's sta...

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Vydané v:IEEE robotics and automation letters Ročník 6; číslo 2; s. 1208 - 1215
Hlavní autori: Spielberg, Andrew, Amini, Alexander, Chin, Lillian, Matusik, Wojciech, Rus, Daniela
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
and learning for soft robots
Cognitive tasks
control
deep learning methods
Graphics
Licenses
Machine learning
Magnetic heads
Magnetization
modeling
Morphology
Performance evaluation
Placement
Plastics
Portable document format
Proprioception
Robot dynamics
Robots
Sensors
Soft robot materials and design
Soft robotics
soft sensors and actuators
Tactile sensors (robotics)
Task complexity
Terminology
ISSN:2377-3766, 2377-3766
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Shrnutí:Unlike rigid robots which operate with compact degrees of freedom, soft robots must reason about an infinite dimensional state space. Mapping this continuum state space presents significant challenges, especially when working with a finite set of discrete sensors. Reconstructing the robot's state from these sparse inputs is challenging, especially since sensor location has a profound downstream impact on the richness of learned models for robotic tasks. In this work, we present a novel representation for co-learning sensor placement and complex tasks. Specifically, we present a neural architecture which processes on-board sensor information to learn a salient and sparse selection of placements for optimal task performance. We evaluate our model and learning algorithm on six soft robot morphologies for various supervised learning tasks, including tactile sensing and proprioception. We also highlight applications to soft robot motion subspace visualization and control. Our method demonstrates superior performance in task learning to algorithmic and human baselines while also learning sensor placements and latent spaces that are semantically meaningful.
Bibliografia:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3056369