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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.18733 |
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| _version_ | 1866915756422201344 |
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| author | Kang, Li Zhou, Heng Song, Xiufeng Li, Rui Chen, Bruno N. Y. Wang, Ziye Meng, Ximeng Tao, Stone Qin, Yiran Liu, Xiaohong Zhang, Ruimao Bai, Lei Du, Yilun Su, Hao Torr, Philip Yin, Zhenfei Gong, Ruihao Zeng, Yejun Zhong, Fengjun Jin, Shenghao Guo, Jinyang Liu, Xianglong Jia, Xiaojun Shan, Tianqi Ren, Wenqi Qin, Simeng Yang, Jialing Ma, Xiaoyu Chen, Tianxing Li, Zixuan Cai, Zijian Qin, Yan Qin, Yusen Chen, Qiangyu Wang, Kaixuan Han, Zhaoming Mu, Yao Luo, Ping Yao, Yuanqi Song, Haoming Zaech, Jan-Nico Despinoy, Fabien Paudel, Danda Pani Van Gool, Luc |
| author_facet | Kang, Li Zhou, Heng Song, Xiufeng Li, Rui Chen, Bruno N. Y. Wang, Ziye Meng, Ximeng Tao, Stone Qin, Yiran Liu, Xiaohong Zhang, Ruimao Bai, Lei Du, Yilun Su, Hao Torr, Philip Yin, Zhenfei Gong, Ruihao Zeng, Yejun Zhong, Fengjun Jin, Shenghao Guo, Jinyang Liu, Xianglong Jia, Xiaojun Shan, Tianqi Ren, Wenqi Qin, Simeng Yang, Jialing Ma, Xiaoyu Chen, Tianxing Li, Zixuan Cai, Zijian Qin, Yan Qin, Yusen Chen, Qiangyu Wang, Kaixuan Han, Zhaoming Mu, Yao Luo, Ping Yao, Yuanqi Song, Haoming Zaech, Jan-Nico Despinoy, Fabien Paudel, Danda Pani Van Gool, Luc |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18733 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge Kang, Li Zhou, Heng Song, Xiufeng Li, Rui Chen, Bruno N. Y. Wang, Ziye Meng, Ximeng Tao, Stone Qin, Yiran Liu, Xiaohong Zhang, Ruimao Bai, Lei Du, Yilun Su, Hao Torr, Philip Yin, Zhenfei Gong, Ruihao Zeng, Yejun Zhong, Fengjun Jin, Shenghao Guo, Jinyang Liu, Xianglong Jia, Xiaojun Shan, Tianqi Ren, Wenqi Qin, Simeng Yang, Jialing Ma, Xiaoyu Chen, Tianxing Li, Zixuan Cai, Zijian Qin, Yan Qin, Yusen Chen, Qiangyu Wang, Kaixuan Han, Zhaoming Mu, Yao Luo, Ping Yao, Yuanqi Song, Haoming Zaech, Jan-Nico Despinoy, Fabien Paudel, Danda Pani Van Gool, Luc Robotics Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.18733 |