MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception
This addresses the challenge of solving complex, open-ended tasks in embodied AI for applications like robotics and simulation, though it appears incremental as it builds on existing MLLM advances.
The authors tackled the problem of designing an embodied system for long-horizon open-world tasks by introducing MP5, a multimodal system in Minecraft, which achieved a 22% success rate on difficult process-dependent tasks and a 91% success rate on context-dependent tasks.
It is a long-lasting goal to design an embodied system that can solve long-horizon open-world tasks in human-like ways. However, existing approaches usually struggle with compound difficulties caused by the logic-aware decomposition and context-aware execution of these tasks. To this end, we introduce MP5, an open-ended multimodal embodied system built upon the challenging Minecraft simulator, which can decompose feasible sub-objectives, design sophisticated situation-aware plans, and perform embodied action control, with frequent communication with a goal-conditioned active perception scheme. Specifically, MP5 is developed on top of recent advances in Multimodal Large Language Models (MLLMs), and the system is modulated into functional modules that can be scheduled and collaborated to ultimately solve pre-defined context- and process-dependent tasks. Extensive experiments prove that MP5 can achieve a 22% success rate on difficult process-dependent tasks and a 91% success rate on tasks that heavily depend on the context. Moreover, MP5 exhibits a remarkable ability to address many open-ended tasks that are entirely novel.