A survey that maps multi-agent embodied AI methods and benchmarks across control, learning, and generative-model categories, and lists open challenges.
RoboCup Rescue 2025 Team Description Paper UruBots
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abstract
This paper describes the approach used by Team UruBots for participation in the 2025 RoboCup Rescue Robot League competition. Our team aims to participate for the first time in this competition at RoboCup, using experience learned from previous competitions and research. We present our vehicle and our approach to tackle the task of detecting and finding victims in search and rescue environments. Our approach contains known topics in robotics, such as ROS, SLAM, Human Robot Interaction and segmentation and perception. Our proposed approach is open source, available to the RoboCup Rescue community, where we aim to learn and contribute to the league.
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cs.AI 1years
2025 1verdicts
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Multi-agent Embodied AI: Advances and Future Directions
A survey that maps multi-agent embodied AI methods and benchmarks across control, learning, and generative-model categories, and lists open challenges.