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Achieving human-like planning and control with multimodal observations in anopen world is a key milestone for more functional generalist agents. Existingapproaches can handle certain long-horizon tasks in an open world. However,they still struggle when the number of open-world tasks could potentially beinfinite and lack the capability to progressively enhance task completion asgame time progresses. We introduce JARVIS-1, an open-world agent that canperceive multimodal input (visual observations and human instructions),generate sophisticated plans, and perform embodied control, all within thepopular yet challenging open-world Minecraft universe. Specifically, we developJARVIS-1 on top of pre-trained multimodal language models, which map visualobservations and textual instructions to plans. The plans will be ultimatelydispatched to the goal-conditioned controllers. We outfit JARVIS-1 with amultimodal memory, which facilitates planning using both pre-trained knowledgeand its actual game survival experiences. JARVIS-1 is the existing most generalagent in Minecraft, capable of completing over 200 different tasks usingcontrol and observation space similar to humans. These tasks range fromshort-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g.,"obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well inshort-horizon tasks, achieving nearly perfect performance. In the classiclong-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses thereliability of current state-of-the-art agents by 5 times and can successfullycomplete longer-horizon and more challenging tasks. The project page isavailable at https://craftjarvis.org/JARVIS-1

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models