End-to-end Differentiable Model of Robot-terrain Interactions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376781" target="_blank" >RIV/68407700:21230/24:00376781 - isvavai.cz</a>

Result on the web

<a href="https://differentiable.xyz/papers-2024/paper_30.pdf" target="_blank" >https://differentiable.xyz/papers-2024/paper_30.pdf</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

End-to-end Differentiable Model of Robot-terrain Interactions

Original language description

We propose a differentiable model of robot-terrain interactions that delivers the expected robot trajectory given an onboard camera image and the robot control. The model is trained on a real dataset that covers various terrains ranging from vegetation to man-made obstacles. Since robot-endangering interactions are naturally absent in real-world training data, the consequent learning of the model suffers from training/testing distribution mismatch, and the quality of the result strongly depends on generalization of the model. Consequently, we propose a grey-box, explainable, physics-aware, and end-to-end differentiable model that achieves better generalization through strong geometrical and physical priors. Our model, which functions as an image-conditioned differentiable simulation, can generate millions of trajectories per second and provides interpretable intermediate outputs that enable efficient self-supervision. Our experimental evaluation demonstrates that the model outperforms state-of-the-art methods.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

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

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)

Publication year

2024

Confidentiality

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