EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control

In response to these limitations, this work introduces a hierarchical Embodied system with an Agent-based planner, named EmbodiedAgent. EmbodiedAgent leverages a next-action prediction paradigm to establish a heterogeneous multi-robot control system. The core agent generates a single action and its corresponding arguments per inference, terminating upon receiving an end-of-planning signal, thus ensuring a controlled and concise execution process. To address the aforementioned challenges, we enhance the planner's robustness and generalizability through supervised fine-tuning. Extended from previous work MultiPlan [4], we present MultiPlan+, a large-scale dataset comprising 100 scenarios with over 18,000 tasks, enriched with a subset of impractical cases to mitigate hallucinations. Additionally, we develop an agent based on a fine-tuned language model equipped with function calling capabilities and structured memory. Specifically, robot skills, termination signals, and error signals related to impractical cases are encapsulated as tools, while planning history is organized within the structured memory. For low-level execution, we employ specialized policies trained on individual basic tasks to ensure reliable and robust performance. Furthermore, we propose a comprehensive R obot Planning A ssessment S chema ( RPAS), which moves beyond error-type diagnostics to emphasize stratified success rates assessed through both human evaluation and automated grading.