REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00388601" target="_blank" >RIV/68407700:21730/25:00388601 - isvavai.cz</a>

Result on the web

<a href="https://openreview.net/pdf?id=O4CQ5AM5yP" target="_blank" >https://openreview.net/pdf?id=O4CQ5AM5yP</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation

Original language description

Sim2real, the transfer of control policies from simulation to the real world, is crucial for efficiently solving robotic tasks without the risks associated with real-world learning. How ever, discrepancies between simulated and real environments, especially due to unmodeled dynamics and latencies, significantly impact the performance of these transferred policies. In this paper, we address the challenges of sim2real transfer caused by latency and asynchronous dynamics in real-world robotic systems. Our approach involves developing a novel framework, REX (Robotic Environments with jaX), that uses a graph-based simulation model to incorporate latency effects while optimizing for parallelization on accelerator hardware. Our framework simulates the asynchronous, hierarchical nature of real-world systems, while simultaneously estimating system dynamics and delays from real-world data and implementing delay compensation strategies to minimize the sim2real gap. We validate our approach on two real-world systems, demonstrating its effectiveness in improving sim2real performance by accurately modeling both system dynamics and delays. Our results show that the proposed framework supports both accelerated simulation and real-time processing, making it valuable for robot learning.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Transactions on Machine Learning Research

ISSN

2835-8856

e-ISSN

2835-8856

Volume of the periodical

2025

Issue of the periodical within the volume

2

Country of publishing house

DE - GERMANY

Number of pages

25

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85219584366