MonoForce: Self-supervised Learning of Physics-informed Model for Predicting Robot-terrain Interaction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376785" target="_blank" >RIV/68407700:21230/24:00376785 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IROS58592.2024.10801353" target="_blank" >https://doi.org/10.1109/IROS58592.2024.10801353</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IROS58592.2024.10801353" target="_blank" >10.1109/IROS58592.2024.10801353</a>
Alternative languages
Result language
angličtina
Original language name
MonoForce: Self-supervised Learning of Physics-informed Model for Predicting Robot-terrain Interaction
Original language description
While autonomous navigation of mobile robots on rigid terrain is a well-explored problem, navigating on deformable terrain such as tall grass or bushes remains a challenge. To address it, we introduce an explainable, physicsaware and end-to-end differentiable model which predicts the outcome of robot-terrain interaction from camera images, both on rigid and non-rigid terrain. The proposed MonoForce model consists of a black-box module which predicts robotterrain interaction forces from onboard cameras, followed by a white-box module, which transforms these forces and a control signals into predicted trajectories, using only the laws of classical mechanics. The differentiable white-box module allows backpropagating the predicted trajectory errors into the black-box module, serving as a self-supervised loss that measures consistency between the predicted forces and groundtruth trajectories of the robot. Experimental evaluation on a public dataset and our data has shown that while the prediction capabilities are comparable to state-of-the-art algorithms on rigid terrain, MonoForce shows superior accuracy on nonrigid terrain such as tall grass or bushes. To facilitate the reproducibility of our results, we release both the code and datasets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
ISBN
979-8-3503-7770-5
ISSN
2153-0858
e-ISSN
2153-0866
Number of pages
8
Pages from-to
12896-12903
Publisher name
IEEE
Place of publication
Piscataway
Event location
Abu Dhabi
Event date
Oct 14, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001433985300697