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Robotics framework bridges sim-to-real gap with informative data collection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel Real-Sim-Real (RSR) framework to address the persistent sim-to-real gap in robotics. This framework incorporates an information-theoretic cost function that balances task completion with the collection of real-world samples most informative for improving policy transfer. The RSR loop can be integrated with existing reinforcement learning algorithms and offers flexibility, treating differentiable simulation as optional. When a differentiable simulator is available, the collected data can also be used to fine-tune simulator parameters. The framework has been demonstrated on both manipulation tasks using a 6-DOF robotic arm and locomotion tasks with a legged robot, showing improved data efficiency and real-world performance. AI

IMPACT Improves efficiency and performance of robotic systems by enhancing sim-to-real transfer capabilities.

RANK_REASON This is a research paper detailing a new framework for robotic policy transfer. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Robotics framework bridges sim-to-real gap with informative data collection

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This is a research paper detailing a new framework for robotic policy transfer. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuxuan Xu, Shiyu Wang, Jinhao Huang, Wenhao Zhao, Yufei Jia, Zike Yan, Weibin Gu, Lu Shi, Guyue Zhou ·

    An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer

    arXiv:2503.10118v3 Announce Type: replace-cross Abstract: The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world systems. We propose a flexible Real-to-Sim-to-Real (RSR) framework whose central cont…

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