Transfer learning in robotics: From promises to practice through the emerging role of foundation models.

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics Pub Date : 2026-04-29 DOI:10.1126/scirobotics.aeh4374

Michael C Welle, Noémie Jaquier, Andrej Gams, Jens Lundell, Danica Kragic

引用次数: 0

Abstract

Foundation models offer a promising avenue to achieve transfer across robots, tasks, and environments.

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机器人技术中的迁移学习：从承诺到实践，通过基础模型的新兴作用。

基础模型为实现机器人、任务和环境之间的转移提供了一个有希望的途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Science Robotics Mathematics-Control and Optimization

CiteScore

30.60

自引率

2.80%

发文量

期刊介绍： Science Robotics publishes original, peer-reviewed, science- or engineering-based research articles that advance the field of robotics. The journal also features editor-commissioned Reviews. An international team of academic editors holds Science Robotics articles to the same high-quality standard that is the hallmark of the Science family of journals. Sub-topics include: actuators, advanced materials, artificial Intelligence, autonomous vehicles, bio-inspired design, exoskeletons, fabrication, field robotics, human-robot interaction, humanoids, industrial robotics, kinematics, machine learning, material science, medical technology, motion planning and control, micro- and nano-robotics, multi-robot control, sensors, service robotics, social and ethical issues, soft robotics, and space, planetary and undersea exploration.