Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge
This work targets the problem of scalable and efficient multi-agent systems for researchers and practitioners in Embodied AI, though it is incremental as it builds on existing frameworks and competitions.
The paper introduces the Multi-Agent Robotic System (MARS) Challenge to address multi-agent collaboration in Embodied AI, focusing on planning and control using vision-language models for robotic manipulation, with insights gained from participant evaluations.
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.