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Auteurs principaux: van Steen, Jari, Stokbroekx, Daan, van de Wouw, Nathan, Saccon, Alessandro
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2411.06319
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author van Steen, Jari
Stokbroekx, Daan
van de Wouw, Nathan
Saccon, Alessandro
author_facet van Steen, Jari
Stokbroekx, Daan
van de Wouw, Nathan
Saccon, Alessandro
contents Impact-aware robotic manipulation benefits from an accurate map from ante-impact to post-impact velocity signals to support, e.g., motion planning and control. This work proposes an approach to generate and experimentally validate such impact maps from simulations with a physics engine, allowing to model impact scenarios of arbitrarily large complexity. This impact map captures the velocity jump assuming an instantaneous contact transition between rigid objects, neglecting the nearly instantaneous contact transition and impact-induced vibrations. Feedback control, which is required for complex impact scenarios, will affect velocity signals when these vibrations are still active, making an evaluation solely based on velocity signals as in previous works unreliable. Instead, the proposed validation approach uses the reference spreading control framework, which aims to reduce peaks and jumps in the control feedback signals by using a reference consistent with the rigid impact map together with a suitable control scheme. Based on the key idea that selecting the correct rigid impact map in this reference spreading framework will minimize the net feedback signal, the rigid impact map is experimentally determined and compared with the impact map obtained from simulation, resulting in a 3.1% average error between the post-impact velocity identified from simulations and from experiments.
format Preprint
id arxiv_https___arxiv_org_abs_2411_06319
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Impact-Aware Robotic Manipulation: Quantifying the Sim-To-Real Gap for Velocity Jumps
van Steen, Jari
Stokbroekx, Daan
van de Wouw, Nathan
Saccon, Alessandro
Robotics
Impact-aware robotic manipulation benefits from an accurate map from ante-impact to post-impact velocity signals to support, e.g., motion planning and control. This work proposes an approach to generate and experimentally validate such impact maps from simulations with a physics engine, allowing to model impact scenarios of arbitrarily large complexity. This impact map captures the velocity jump assuming an instantaneous contact transition between rigid objects, neglecting the nearly instantaneous contact transition and impact-induced vibrations. Feedback control, which is required for complex impact scenarios, will affect velocity signals when these vibrations are still active, making an evaluation solely based on velocity signals as in previous works unreliable. Instead, the proposed validation approach uses the reference spreading control framework, which aims to reduce peaks and jumps in the control feedback signals by using a reference consistent with the rigid impact map together with a suitable control scheme. Based on the key idea that selecting the correct rigid impact map in this reference spreading framework will minimize the net feedback signal, the rigid impact map is experimentally determined and compared with the impact map obtained from simulation, resulting in a 3.1% average error between the post-impact velocity identified from simulations and from experiments.
title Impact-Aware Robotic Manipulation: Quantifying the Sim-To-Real Gap for Velocity Jumps
topic Robotics
url https://arxiv.org/abs/2411.06319