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How can agents understand the world from diverse language? ๐ŸŒŽ Excited to introduce Dynalang, an agent that learns to understand language by ๐™ข๐™–๐™ ๐™ž๐™ฃ๐™œ ๐™ฅ๐™ง๐™š๐™™๐™ž๐™˜๐™ฉ๐™ž๐™ค๐™ฃ๐™จ ๐™–๐™—๐™ค๐™ช๐™ฉ ๐™ฉ๐™๐™š ๐™›๐™ช๐™ฉ๐™ช๐™ง๐™š with a multimodal world model!

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JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models paper page: Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy.

AK

141,440 Aufrufe โ€ข vor 2 Jahren