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#176 Sergey Levine: Decoding The Evolution of AI in Robotics
Eye On A.I.
Outlook on Generalization of Models for Robotics
Robotics research must transition towards a model-sharing paradigm similar to that seen in fields like computer vision and natural language processing. Currently, the focus is on producing code, papers, or insights, rather than portable models that can be reused across different laboratories or contexts. To foster progress, it's crucial to create models trained on datasets that allow for generalization across various environments, tasks, and objects. The establishment of shared datasets, such as the RTX dataset featuring multiple robots, can facilitate this process. This communal approach could eventually lead to the emergence of a standardized pre-trained backbone for robotics, akin to models in NLP, which researchers could then build upon. However, progress hinges on developing reusable models that all researchers can access and run, paving the way for a collaborative and advancing robotics community.
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