actor-critic-model-predictive-force-controller/CITATION.cff at release · unisa-acg/actor-critic-model-predictive-force-controller · GitHub

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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Experimental Validation of an Actor-Critic Model
  Predictive Force Controller for Robot-Environment
  Interaction Tasks
message: 'Related paper: http://dx.doi.org/10.5220/0012160700003543'
type: software
authors:
  - given-names: Alessandro
    family-names: Pozzi
    email: alessandro11.pozzi@mail.polimi.it
    affiliation: Politecnico di Milano
    orcid: 'https://orcid.org/0000-0002-6460-4669'
  - given-names: Luca
    family-names: Puricelli
    email: luca.puricelli@mail.polimi.it
    affiliation: Politecnico di Milano
    orcid: 'https://orcid.org/0000-0003-1736-0822'
  - given-names: Vincenzo
    family-names: Petrone
    email: vipetrone@unisa.it
    affiliation: University of Salerno
    orcid: 'https://orcid.org/0000-0003-4777-1761'
  - given-names: Enrico
    family-names: Ferrentino
    email: eferrentino@unisa.it
    affiliation: University of Salerno
    orcid: 'https://orcid.org/0000-0003-0768-8541'
  - given-names: Pasquale
    family-names: Chiacchio
    email: pchiacchio@unisa.it
    affiliation: University of Salerno
    orcid: 'https://orcid.org/0000-0003-3385-8866'
  - given-names: Francesco
    family-names: Braghin
    email: francesco.braghin@polimi.it
    affiliation: Politecnico di Milano
    orcid: 'https://orcid.org/0000-0002-0476-4118'
  - given-names: Loris
    family-names: Roveda
    email: loris.roveda@supsi.ch
    affiliation: IDSIA USI-SUPSI
    orcid: 'https://orcid.org/0000-0002-4427-536X'
identifiers:
  - type: doi
    value: 10.5220/0012160700003543
    description: >-
      Related paper published in In Proceedings of the 20th
      International Conference on Informatics in Control,
      Automation and Robotics (ICINCO 2023) - Volume 1,
      pages 394-404
repository-code: >-
  https://github.com/unisa-acg/actor-critic-model-predictive-force-controller
url: 'https://www.youtube.com/watch?v=7ysG4lz5lVY'
abstract: >-
  In industrial settings, robots are typically employed to
  accurately track a reference force to exert on the sur-
  rounding environment to complete interaction tasks.
  Interaction controllers are typically used to achieve this
  goal. Still, they either require manual tuning, which
  demands a significant amount of time, or exact modeling of
  the environment the robot will interact with, thus
  possibly failing during the actual application. A
  significant advancement in this area would be a
  high-performance force controller that does not need
  operator calibration and is quick to be deployed in any
  scenario. With this aim, this paper proposes an
  Actor-Critic Model Pre- dictive Force Controller (ACMPFC),
  which outputs the optimal setpoint to follow in order to
  guarantee force tracking, computed by continuously trained
  neural networks. This strategy is an extension of a
  reinforcement learning-based one, born in the context of
  human-robot collaboration, suitably adapted to
  robot-environment interaction. We validate the ACMPFC in a
  real-case scenario featuring a Franka Emika Panda robot.
  Com- pared with a base force controller and a
  learning-based approach, the proposed controller yields a
  reduction of the force tracking MSE, attaining fast
  convergence: with respect to the base force controller,
  ACMPFC reduces the MSE by a factor of 4.35.
keywords:
  - physical robot-environment interaction
  - artificial neural networks
  - optimized interaction control
  - impedance control
license: BSD-3-Clause
commit: d45c57be51106c66d2013cfbc040bd4e8ca6d6d6
date-released: '2023-02-20'