SIMA 2: A Generalist Embodied Agent for Virtual Worlds
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a significant step toward active, goal-directed interaction within an embodied environment. Unlike prior work (e.g., SIMA 1) limited to simple language commands, SIMA 2 acts as an interactive partner, capable of reasoning about high-level goals, conversing with the user, and handling complex instructions given through language and images. Across a diverse portfolio of games, SIMA 2 substantially closes the gap with human performance and demonstrates robust generalization to previously unseen environments, all while retaining the base model's core reasoning capabilities. Furthermore, we demonstrate a capacity for open-ended self-improvement: by leveraging Gemini to generate tasks and provide rewards, SIMA 2 can autonomously learn new skills from scratch in a new environment. This work validates a path toward creating versatile and continuously learning agents for both virtual and, eventually, physical worlds.
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SIMA team
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Adrian Bolton
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Alexander Lerchner
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Alexandra Cordell
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Alexandre Moufarek
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Andrew Bolt
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Andrew Lampinen
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Anna Mitenkova
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Arne Olav Hallingstad
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Bojan Vujatovic
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Bonnie Li
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Cong Lu
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Daan Wierstra
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Daniel P. Sawyer
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Daniel Slater
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David Reichert
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Davide Vercelli
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Demis Hassabis
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Drew A. Hudson
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Duncan Williams
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Ed Hirst
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Fabio Pardo
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Felix Hill
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Frederic Besse
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Hannah Openshaw
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Harris Chan
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Hubert Soyer
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Jane X. Wang
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Jeff Clune
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John Agapiou
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John Reid
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Joseph Marino
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Junkyung Kim
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Karol Gregor
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Kaustubh Sridhar
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Kay McKinney
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Laura Kampis
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Lei M. Zhang
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Loic Matthey
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Luyu Wang
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Maria Abi Raad
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Maria Loks-Thompson
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Martin Engelcke
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Matija Kecman
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Matthew Jackson
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Maxime Gazeau
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Ollie Purkiss
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Oscar Knagg
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Peter Stys
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Piermaria Mendolicchio
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Raia Hadsell
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Rosemary Ke
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Ryan Faulkner
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Sarah Chakera
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Satinder Singh Baveja
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Shane Legg
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Sheleem Kashem
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Tayfun Terzi
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Thomas Keck
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Tim Harley
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Tim Scholtes
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Tyson Roberts
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Volodymyr Mnih
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Yulan Liu
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Zhengdong Wang
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Zoubin Ghahramani
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a significant step toward active, goal-directed interaction within an embodied environment. Unlike prior work (e.g., SIMA 1) limited to simple language commands, SIMA 2 acts as an interactive partner, capable of reasoning about high-level goals, conversing with the user, and handling complex instructions given through language and images. Across a diverse portfolio of games, SIMA 2 substantially closes the gap with human performance and demonstrates robust generalization to previously unseen environments, all while retaining the base model's core reasoning capabilities. Furthermore, we demonstrate a capacity for open-ended self-improvement: by leveraging Gemini to generate tasks and provide rewards, SIMA 2 can autonomously learn new skills from scratch in a new environment. This work validates a path toward creating versatile and continuously learning agents for both virtual and, eventually, physical worlds.
SIMA 2, built on a Gemini foundation model, interacts in 3D virtual worlds, reasons about goals, handles complex instructions, and autonomously learns new skills through open-ended self-improvement.
Gemini foundation model embodied agent high-level goals conversational abilities complex instructions language and images generalization open-ended self-improvement task generation reward provision autonomous learning