JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal
  Language Models
Authors: name: Zihao Wang, name: Shaofei Cai, name: Anji Liu, name: Yonggang Jin, name: Jinbing Hou, name: Bowei Zhang, name: Haowei Lin, name: Zhaofeng He, name: Zilong Zheng, name: Yaodong Yang, name: Xiaojian Ma, name: Yitao Liang

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. The project page is available
at https://craftjarvis-jarvis1.github.io.