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Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot

Buzzwire tasks are often used as benchmarks and as training environments for fine motor skills and high precision path following. These tasks require moving a wire loop along an arbitrarily shaped wire obstacle in a collision-free manner. While there have been some demonstrations of buzzwire tasks w...

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Autores principales: Lee, Peter Q., Rajendran, Vidyasagar, Mombaur, Katja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203844/
https://www.ncbi.nlm.nih.gov/pubmed/35719206
http://dx.doi.org/10.3389/frobt.2022.898890
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author Lee, Peter Q.
Rajendran, Vidyasagar
Mombaur, Katja
author_facet Lee, Peter Q.
Rajendran, Vidyasagar
Mombaur, Katja
author_sort Lee, Peter Q.
collection PubMed
description Buzzwire tasks are often used as benchmarks and as training environments for fine motor skills and high precision path following. These tasks require moving a wire loop along an arbitrarily shaped wire obstacle in a collision-free manner. While there have been some demonstrations of buzzwire tasks with robotic manipulators using reinforcement learning and admittance control, there does not seem to be any examples with humanoid robots. In this work, we consider the scenario where we control one arm of the REEM-C humanoid robot, with other joints fixed, as groundwork for eventual full-body control. In pursuit of this, we contribute by designing an optimal control problem that generates trajectories to solve the buzzwire in a time optimized manner. This is composed of task-space constraints to prevent collisions with the buzzwire obstacle, the physical limits of the robot, and an objective function to trade-off reducing time and increasing margins from collision. The formulation can be applied to a very general set of wire shapes and the objective and task constraints can be adapted to other hardware configurations. We evaluate this formulation using the arm of a REEM-C humanoid robot and provide an analysis of how the generated trajectories perform both in simulation and on hardware.
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spelling pubmed-92038442022-06-18 Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot Lee, Peter Q. Rajendran, Vidyasagar Mombaur, Katja Front Robot AI Robotics and AI Buzzwire tasks are often used as benchmarks and as training environments for fine motor skills and high precision path following. These tasks require moving a wire loop along an arbitrarily shaped wire obstacle in a collision-free manner. While there have been some demonstrations of buzzwire tasks with robotic manipulators using reinforcement learning and admittance control, there does not seem to be any examples with humanoid robots. In this work, we consider the scenario where we control one arm of the REEM-C humanoid robot, with other joints fixed, as groundwork for eventual full-body control. In pursuit of this, we contribute by designing an optimal control problem that generates trajectories to solve the buzzwire in a time optimized manner. This is composed of task-space constraints to prevent collisions with the buzzwire obstacle, the physical limits of the robot, and an objective function to trade-off reducing time and increasing margins from collision. The formulation can be applied to a very general set of wire shapes and the objective and task constraints can be adapted to other hardware configurations. We evaluate this formulation using the arm of a REEM-C humanoid robot and provide an analysis of how the generated trajectories perform both in simulation and on hardware. Frontiers Media S.A. 2022-06-03 /pmc/articles/PMC9203844/ /pubmed/35719206 http://dx.doi.org/10.3389/frobt.2022.898890 Text en Copyright © 2022 Lee, Rajendran and Mombaur. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Lee, Peter Q.
Rajendran, Vidyasagar
Mombaur, Katja
Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot
title Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot
title_full Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot
title_fullStr Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot
title_full_unstemmed Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot
title_short Optimization-Based Motion Generation for Buzzwire Tasks With the REEM-C Humanoid Robot
title_sort optimization-based motion generation for buzzwire tasks with the reem-c humanoid robot
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203844/
https://www.ncbi.nlm.nih.gov/pubmed/35719206
http://dx.doi.org/10.3389/frobt.2022.898890
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