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 [BibTeX] [Marc21]
Ergodic Exploration using Tensor Train: Applications in Insertion Tasks
Type of publication: Journal paper
Citation: Shetty21TRO
Publication status: Published
Journal: IEEE Transactions on Robotics
Volume: 38
Number: 2
Year: 2022
Month: March
Pages: 906--921
URL: https://ieeexplore.ieee.org/do...
DOI: 10.1109/TRO.2021.3087317
Abstract: In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring the spatial distribution to be decomposed as Fourier series. This approach does not scale well to control problems requiring exploration in search space of more than 2 dimensions. To address this issue, we propose the use of tensor trains, a recent low-rank tensor decomposition technique from the field of multilinear algebra. The proposed solution is efficient, both computationally and storage-wise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task requiring full 6D end-effector poses, implemented with a 7-axis Franka Emika Panda robot. In this experiment, ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors.
Projects Idiap
Authors Shetty, Suhan
Silverio, J.
Calinon, Sylvain
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Total mark: 0
  • Shetty21TRO.pdf