Gemini Robotics: Bringing AI into the Physical World
Recent advancements in large multimodal models have led to the emergence of
remarkable generalist capabilities in digital domains, yet their translation to
physical agents such as robots remains a significant challenge. This report
introduces a new family of AI models purposefully designed for robotics and
built upon the foundation of Gemini 2.0. We present Gemini Robotics, an
advanced Vision-Language-Action (VLA) generalist model capable of directly
controlling robots. Gemini Robotics executes smooth and reactive movements to
tackle a wide range of complex manipulation tasks while also being robust to
variations in object types and positions, handling unseen environments as well
as following diverse, open vocabulary instructions. We show that with
additional fine-tuning, Gemini Robotics can be specialized to new capabilities
including solving long-horizon, highly dexterous tasks, learning new
short-horizon tasks from as few as 100 demonstrations and adapting to
completely novel robot embodiments. This is made possible because Gemini
Robotics builds on top of the Gemini Robotics-ER model, the second model we
introduce in this work. Gemini Robotics-ER (Embodied Reasoning) extends
Gemini's multimodal reasoning capabilities into the physical world, with
enhanced spatial and temporal understanding. This enables capabilities relevant
to robotics including object detection, pointing, trajectory and grasp
prediction, as well as multi-view correspondence and 3D bounding box
predictions. We show how this novel combination can support a variety of
robotics applications. We also discuss and address important safety
considerations related to this new class of robotics foundation models. The
Gemini Robotics family marks a substantial step towards developing
general-purpose robots that realizes AI's potential in the physical world.
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Gemini Robotics Team
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Saminda Abeyruwan
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2025-03-27T09:58:16.630Z
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Saminda Abeyruwan
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Joshua Ainslie
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Jean-Baptiste Alayrac
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Jean-Baptiste Alayrac
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Montserrat Gonzalez Arenas
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2025-03-27T09:58:33.087Z
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Montserrat Gonzalez Arenas
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Travis Armstrong
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Travis Armstrong
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Ashwin Balakrishna
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Ashwin Balakrishna
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Robert Baruch
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Maria Bauza
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Michiel Blokzijl
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Konstantinos Bousmalis
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Anthony Brohan
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Arunkumar Byravan
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Serkan Cabi
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Oscar Chang
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Jose Enrique Chen
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Xi Chen
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Hao-Tien Lewis Chiang
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Krzysztof Choromanski
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David D'Ambrosio
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Sudeep Dasari
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Todor Davchev
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Coline Devin
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Norman Di Palo
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Norman Di Palo
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Tianli Ding
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Tianli Ding
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Adil Dostmohamed
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2025-03-27T10:00:29.517Z
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Adil Dostmohamed
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Danny Driess
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Danny Driess
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Yilun Du
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2025-03-27T10:00:43.648Z
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Yilun Du
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Debidatta Dwibedi
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Michael Elabd
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Michael Elabd
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Claudio Fantacci
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Cody Fong
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Erik Frey
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Chuyuan Fu
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Keerthana Gopalakrishnan
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Leonard Hasenclever
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Nicolas Heess
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Alexander Herzog
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R. Alex Hofer
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Jan Humplik
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Atil Iscen
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Dmitry Kalashnikov
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Dmitry Kalashnikov
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Thomas Lampe
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Alex X. Lee
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Jacky Liang
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Yixin Lin
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Sharath Maddineni
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Anirudha Majumdar
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Assaf Hurwitz Michaely
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Robert Moreno
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Carolina Parada
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Peter Pastor
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Acorn Pooley
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Kanishka Rao
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Krista Reymann
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Dorsa Sadigh
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Mohit Sharma
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Charles Shu
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Vikas Sindhwani
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Radu Soricut
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Fei Xia
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Fei Xia
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Annie Xie
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Ying Xu
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Zhuo Xu
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Yuxiang Yang
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Jingwei Zhang
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Tingnan Zhang
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Allan Zhou
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Yuxiang Zhou
Gemini Robotics: Bringing AI into the Physical World
Recent advancements in large multimodal models have led to the emergence of
remarkable generalist capabilities in digital domains, yet their translation to
physical agents such as robots remains a significant challenge. This report
introduces a new family of AI models purposefully designed for robotics and
built upon the foundation of Gemini 2.0. We present Gemini Robotics, an
advanced Vision-Language-Action (VLA) generalist model capable of directly
controlling robots. Gemini Robotics executes smooth and reactive movements to
tackle a wide range of complex manipulation tasks while also being robust to
variations in object types and positions, handling unseen environments as well
as following diverse, open vocabulary instructions. We show that with
additional fine-tuning, Gemini Robotics can be specialized to new capabilities
including solving long-horizon, highly dexterous tasks, learning new
short-horizon tasks from as few as 100 demonstrations and adapting to
completely novel robot embodiments. This is made possible because Gemini
Robotics builds on top of the Gemini Robotics-ER model, the second model we
introduce in this work. Gemini Robotics-ER (Embodied Reasoning) extends
Gemini's multimodal reasoning capabilities into the physical world, with
enhanced spatial and temporal understanding. This enables capabilities relevant
to robotics including object detection, pointing, trajectory and grasp
prediction, as well as multi-view correspondence and 3D bounding box
predictions. We show how this novel combination can support a variety of
robotics applications. We also discuss and address important safety
considerations related to this new class of robotics foundation models. The
Gemini Robotics family marks a substantial step towards developing
general-purpose robots that realizes AI's potential in the physical world.
Gemini Robotics, an advanced Vision-Language-Action model based on Gemini 2.0, controls robots with multimodal reasoning, handling complex tasks and adapting to new environments and instructions.
Vision-Language-Action multimodal reasoning spatial and temporal understanding object detection pointing trajectory prediction grasp prediction multi-view correspondence 3D bounding box predictions embodied reasoning