TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft (2412.05255v1)

Abstract: Collaboration is a cornerstone of society. In the real world, human teammates make use of multi-sensory data to tackle challenging tasks in ever-changing environments. It is essential for embodied agents collaborating in visually-rich environments replete with dynamic interactions to understand multi-modal observations and task specifications. To evaluate the performance of generalizable multi-modal collaborative agents, we present TeamCraft, a multi-modal multi-agent benchmark built on top of the open-world video game Minecraft. The benchmark features 55,000 task variants specified by multi-modal prompts, procedurally-generated expert demonstrations for imitation learning, and carefully designed protocols to evaluate model generalization capabilities. We also perform extensive analyses to better understand the limitations and strengths of existing approaches. Our results indicate that existing models continue to face significant challenges in generalizing to novel goals, scenes, and unseen numbers of agents. These findings underscore the need for further research in this area. The TeamCraft platform and dataset are publicly available at https://github.com/teamcraft-bench/teamcraft.

• The paper introduces TeamCraft, a novel benchmark using Minecraft with 55,000 multi-modal task variants to evaluate multi-agent systems' generalization in complex environments.
• Evaluations using TeamCraft reveal that current models struggle significantly with generalization to novel tasks, unseen scenes, and varying agent numbers, showing less than 50% success.
• The benchmark encourages further research into enhancing agent collaboration, improving multi-modal understanding, and developing more robust agents for decentralized settings.

An Analysis of "TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft"

The paper, "TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft," addresses the development of collaborative skills for embodied agents in multi-modal and multi-agent environments. By leveraging the open-world game Minecraft, the authors present a novel benchmark designed to evaluate the generalization capabilities of multi-agent systems handling diverse tasks driven by multi-modal prompts. This benchmark serves as a comprehensive platform for assessing agent collaboration within visually rich, procedurally generated environments— a significant enhancement over existing grid-world environments that often lack complexity.

The TeamCraft benchmark includes 55,000 task variants specified by multi-modal prompts with visual and textual elements, making it an extensive dataset compared to others. Task structures span different domains such as building, clearing, farming, and smelting, each presenting unique challenges due to the variability in task specifications, environmental dynamics, agent numbers, and agent capabilities. Expert demonstrations are provided to support imitation learning, a crucial approach when training such agents.

Numerical results from the paper highlight persistent challenges in achieving robust generalization capabilities. Existing models particularly struggle with novel task goals, unseen scenes, and varying numbers of agents, significantly underperforming in these conditions. For instance, models evaluated using TeamCraft showed less than 50% task success rate when dealing with tasks requiring generalization beyond the training conditions. This underscores the need for continued research around more adaptive and intelligent models capable of abstracting task dynamics and spatial reasoning from visual inputs.

The benchmark's implications go beyond assessing task performance. It encourages the development of techniques for better understanding and inferring complex tasks from multi-modal instructions. Additionally, the benchmark can guide investigations into communication mechanisms between agents to enhance cooperation, especially under decentralized settings where agents have only partial observations of the environment.

In terms of future research directions, there is potential for exploration within several areas:

1. Enhancing vision-based agents' ability to interpret complex visual cues and infer tasks more effectively.
2. Drawing insights from human-agent interaction research to improve agent coordination and task allocation.
3. Expanding the benchmark with real-world noisy demonstrations to simulate more realistic conditions, providing a robust testbed for further advances in multi-agent systems.

In conclusion, the TeamCraft benchmark establishes a significant step forward in evaluating and developing multi-agent systems in multi-modal environments. By challenging current models and identifying key areas for improvement, it paves the way for advancing the collaborative capabilities of AI systems, setting the groundwork for future developments in both theoretical and practical segments of AI research.