A roadmap for AI in robotics
Aude Billard, Alin Albu-Schaeffer, Michael Beetz, Wolfram Burgard, Peter Corke, Matei Ciocarlie, Ravinder Dahiya, Danica Kragic, Ken Goldberg, Yukie Nagai, Davide Scaramuzza

TL;DR
This paper reviews the progress of AI in robotics since the 1990s, identifies key challenges, and proposes a research roadmap focusing on datasets, adaptable algorithms, human-robot collaboration, explainability, and lifelong learning.
Contribution
It provides a comprehensive assessment of AI in robotics and outlines a strategic research roadmap addressing current and future challenges.
Findings
AI has advanced significantly in robotics since the 1990s.
Key challenges include dataset diversity, algorithm adaptability, and human-robot interaction.
Long-term goals involve lifelong learning and safe, sustainable deployment.
Abstract
AI technologies, including deep learning, large-language models have gone from one breakthrough to the other. As a result, we are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives. However, action and sensing in the physical world pose greater and different challenges than analysing data in isolation. As the development and application of AI in robotic products advances, it is important to reflect on which technologies, among the vast array of network architectures and learning models now available in the AI field, are most likely to be successfully applied to robots; how they can be adapted to specific robot designs, tasks, environments; which challenges must be overcome. This article offers an assessment of what AI for robotics has achieved since the…
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