Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence

While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large language models (LLMs) and vision language models (VLMs), opens the possibility of teams that infer mission-relevant semantics and subtasks given high-level natural language specifications and environmental context. However, current LLM- and VLM-enabled teaming methods typically assume well-structured and known environments, limiting performance in complex real-world settings. We address these limitations via SPINE-HT, a framework that grounds the reasoning abilities of LLMs in the evolving context of a heterogeneous robot team through a three-stage process. Given mission specifications and team capabilities in natural language, an LLM infers necessary subtasks. These subtasks are validated for feasibility and then assigned to robots based on capabilities such as traversability or perception. Finally, the framework uses feedback (semantic maps and task outcomes) acquired by the robots to update subtasks online. In simulation experiments with closed-loop perception and control, our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches. In real-world experiments with a Clearpath Jackal, a Clearpath Husky, a Boston Dynamics Spot, and a high-altitude UAV, our method achieves an 87.5% success rate in missions requiring reasoning about robot capabilities and refining subtasks with online feedback. More information is provided at: https://zacravichandran.github.io/SPINE-HT.