Improving Cooperation in Collaborative Embodied AI
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Suprabha, Hima Jacob Leven Nagesh, Laxmi Nag Laxminarayan Nair, Ajith Selvaster, Alvin Reuben Amal Khan, Ayan Damarla, Raghuram Samuel, Sanju Hannah Perumal, Sreenithi Saravana Puech, Titouan Marella, Venkataramireddy Sonar, Vishal Suglia, Alessandro Lemon, Oliver |
| author_facet | Suprabha, Hima Jacob Leven Nagesh, Laxmi Nag Laxminarayan Nair, Ajith Selvaster, Alvin Reuben Amal Khan, Ayan Damarla, Raghuram Samuel, Sanju Hannah Perumal, Sreenithi Saravana Puech, Titouan Marella, Venkataramireddy Sonar, Vishal Suglia, Alessandro Lemon, Oliver |
| contents | The integration of Large Language Models (LLMs) into multiagent systems has opened new possibilities for collaborative reasoning and cooperation with AI agents. This paper explores different prompting methods and evaluates their effectiveness in enhancing agent collaborative behaviour and decision-making. We enhance CoELA, a framework designed for building Collaborative Embodied Agents that leverage LLMs for multi-agent communication, reasoning, and task coordination in shared virtual spaces. Through systematic experimentation, we examine different LLMs and prompt engineering strategies to identify optimised combinations that maximise collaboration performance. Furthermore, we extend our research by integrating speech capabilities, enabling seamless collaborative voice-based interactions. Our findings highlight the effectiveness of prompt optimisation in enhancing collaborative agent performance; for example, our best combination improved the efficiency of the system running with Gemma3 by 22% compared to the original CoELA system. In addition, the speech integration provides a more engaging user interface for iterative system development and demonstrations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Cooperation in Collaborative Embodied AI Suprabha, Hima Jacob Leven Nagesh, Laxmi Nag Laxminarayan Nair, Ajith Selvaster, Alvin Reuben Amal Khan, Ayan Damarla, Raghuram Samuel, Sanju Hannah Perumal, Sreenithi Saravana Puech, Titouan Marella, Venkataramireddy Sonar, Vishal Suglia, Alessandro Lemon, Oliver Artificial Intelligence Multiagent Systems Robotics The integration of Large Language Models (LLMs) into multiagent systems has opened new possibilities for collaborative reasoning and cooperation with AI agents. This paper explores different prompting methods and evaluates their effectiveness in enhancing agent collaborative behaviour and decision-making. We enhance CoELA, a framework designed for building Collaborative Embodied Agents that leverage LLMs for multi-agent communication, reasoning, and task coordination in shared virtual spaces. Through systematic experimentation, we examine different LLMs and prompt engineering strategies to identify optimised combinations that maximise collaboration performance. Furthermore, we extend our research by integrating speech capabilities, enabling seamless collaborative voice-based interactions. Our findings highlight the effectiveness of prompt optimisation in enhancing collaborative agent performance; for example, our best combination improved the efficiency of the system running with Gemma3 by 22% compared to the original CoELA system. In addition, the speech integration provides a more engaging user interface for iterative system development and demonstrations. |
| title | Improving Cooperation in Collaborative Embodied AI |
| topic | Artificial Intelligence Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2510.03153 |