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[2602.19304] Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation
Summary
The paper presents CaPE, a multimodal path planning method that enhances cooperation among decentralized agents through language communication, ensuring safety and interpretability in dynamic environments.
Why It Matters
As multi-agent systems become more prevalent in robotics and autonomous applications, ensuring safe and effective cooperation is crucial. This research addresses the challenge of path planning in uncertain environments, making it relevant for advancements in AI safety and human-robot interaction.
Key Takeaways
- CaPE integrates language communication for safe path planning among agents.
- The method enhances adaptability in dynamic environments, reducing collision risks.
- Experimental results show CaPE's effectiveness in real-world scenarios, including human-robot cooperation.
Computer Science > Robotics arXiv:2602.19304 (cs) [Submitted on 22 Feb 2026] Title:Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation Authors:Haojun Shi, Suyu Ye, Katherine M. Guerrerio, Jianzhi Shen, Yifan Yin, Daniel Khashabi, Chien-Ming Huang, Tianmin Shu View a PDF of the paper titled Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation, by Haojun Shi and 7 other authors View PDF HTML (experimental) Abstract:Successful cooperation among decentralized agents requires each agent to quickly adapt its plan to the behavior of other agents. In scenarios where agents cannot confidently predict one another's intentions and plans, language communication can be crucial for ensuring safety. In this work, we focus on path-level cooperation in which agents must adapt their paths to one another in order to avoid collisions or perform physical collaboration such as joint carrying. In particular, we propose a safe and interpretable multimodal path planning method, CaPE (Code as Path Editor), which generates and updates path plans for an agent based on the environment and language communication from other agents. CaPE leverages a vision-language model (VLM) to synthesize a path editing program verified by a model-based planner, grounding communication to path plan updates in a safe and interpretable way. We evaluate our approach in diverse simulated and real-world scenarios, including multi-robot and human-robot cooperation in autonomous ...
