NeurIPS 2025 MARS挑战赛推动多智能体机器人协同规划与控制创新
Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge
- 基于视觉语言模型实现多机器人任务协同规划
- 在动态环境中完成机器人操作任务的策略执行
- 适合关注多智能体协作与具身AI的开发者和研究者
多模态大语言模型与视觉-语言-动作模型的进展显著推动了具身人工智能的发展。随着任务场景日益复杂，多智能体系统框架成为实现可扩展、高效且协作式解决方案的关键。这一趋势由三大因素驱动：智能体能力提升、通过任务委派提高系统效率，以及实现高级人机交互。为此，我们在NeurIPS 2025 SpaVLE研讨会中发起多智能体机器人系统（MARS）挑战赛，聚焦于规划与控制两大核心领域。参赛者利用视觉语言模型（VLMs）进行多智能体具身规划，并在动态环境中执行机器人操控策略。通过评估参赛方案，挑战赛为具身多智能体系统的架构设计与协调提供了宝贵洞见，助力先进协作式AI系统的未来发展。
原文摘要 · Abstract (English)
Recent advancements in multimodal large language models and vision-languageaction models have significantly driven progress in Embodied AI. As the field transitions toward more complex task scenarios, multi-agent system frameworks are becoming essential for achieving scalable, efficient, and collaborative solutions. This shift is fueled by three primary factors: increasing agent capabilities, enhancing system efficiency through task delegation, and enabling advanced human-agent interactions. To address the challenges posed by multi-agent collaboration, we propose the Multi-Agent Robotic System (MARS) Challenge, held at the NeurIPS 2025 Workshop on SpaVLE. The competition focuses on two critical areas: planning and control, where participants explore multi-agent embodied planning using vision-language models (VLMs) to coordinate tasks and policy execution to perform robotic manipulation in dynamic environments. By evaluating solutions submitted by participants, the challenge provides valuable insights into the design and coordination of embodied multi-agent systems, contributing to the future development of advanced collaborative AI systems.
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