Differentiable Collision Detection: a Randomized Smoothing Approach
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21730/23:00371945
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Differentiable Collision Detection: a Randomized Smoothing Approach
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE International Conference on Robotics and Automation
ISBN
979-8-3503-2365-8
ISSN
1050-4729
e-ISSN
2577-087X
Number of pages
7
Pages from-to
3240-3246
Publisher name
IEEE
Place of publication
Piscataway
Event location
Londýn
Event date
May 29, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001036713002089