Octopus: Embodied Vision-Language Programmer from Environmental Feedback
Jingkang Yang Yuhao Dong Shuai Liu Bo Li Ziyue Wang Chencheng Jiang Haoran Tan Jiamu Kang Yuanhan Zhang Kaiyang Zhou Ziwei Liu
Large vision-language models (VLMs) have achieved substantial progress in
multimodal perception and reasoning. Furthermore, when seamlessly integrated
into an embodied agent, it signifies a crucial stride towards the creation of
autonomous and context-aware systems capable of formulating plans and executing
commands with precision. In this paper, we introduce Octopus, a novel VLM
designed to proficiently decipher an agent's vision and textual task objectives
and to formulate intricate action sequences and generate executable code. Our
design allows the agent to adeptly handle a wide spectrum of tasks, ranging
from mundane daily chores in simulators to sophisticated interactions in
complex video games. Octopus is trained by leveraging GPT-4 to control an
explorative agent to generate training data, i.e., action blueprints and the
corresponding executable code, within our experimental environment called
OctoVerse. We also collect the feedback that allows the enhanced training
scheme of Reinforcement Learning with Environmental Feedback (RLEF). Through a
series of experiments, we illuminate Octopus's functionality and present
compelling results, and the proposed RLEF turns out to refine the agent's
decision-making. By open-sourcing our model architecture, simulator, and
dataset, we aspire to ignite further innovation and foster collaborative
applications within the broader embodied AI community.