Differentiable Collision Detection: a Randomized Smoothing Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00371945" target="_blank" >RIV/68407700:21230/23:00371945 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00371945

Výsledek na webu

<a href="https://doi.org/10.1109/ICRA48891.2023.10160251" target="_blank" >https://doi.org/10.1109/ICRA48891.2023.10160251</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICRA48891.2023.10160251" target="_blank" >10.1109/ICRA48891.2023.10160251</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Differentiable Collision Detection: a Randomized Smoothing Approach

Popis výsledku v původním jazyce

Collision detection is an important component of many robotics applications, from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications, including differentiable simulation.

Název v anglickém jazyce

Differentiable Collision Detection: a Randomized Smoothing Approach

Popis výsledku anglicky

Collision detection is an important component of many robotics applications, from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications, including differentiable simulation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Inteligentní strojové vnímání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE International Conference on Robotics and Automation

ISBN

979-8-3503-2365-8

ISSN

1050-4729

e-ISSN

2577-087X

Počet stran výsledku

7

Strana od-do

3240-3246

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Londýn

Datum konání akce

29. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001036713002089