実世界の非構造環境における異種ロボット協調のための接地型生成知能
Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence
LLMの推論を異種ロボットチームの能力と物理世界に接地させ、自然言語で与えられた複雑なミッションをサブタスク生成・割当・オンライン修正の3段階で遂行するフレームワークSPINE-HTを提案。
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著者: Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes, Varun Murali, Camillo Taylor, George J. Pappas, Vijay Kumar
分類: cs.RO, cs.AI
原文アブストラクト
Heterogeneous robot teams operating in realistic settings often must accomplish complex missions requiring collaboration and adaptation to information acquired online. Because robot teams frequently operate in unstructured environments -- uncertain, open-world settings without prior maps -- subtasks must be grounded in robot capabilities and the physical world. While heterogeneous teams have typically been designed for fixed specifications, generative intelligence opens the possibility of teams that can accomplish a wide range of missions described in natural language. However, current large language model (LLM)-enabled teaming methods typically assume well-structured and known environments, limiting deployment in unstructured environments. We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team through a three-stage process. Given language specifications describing mission goals and team capabilities, an LLM generates grounded subtasks which are validated for feasibility. Subtasks are then assigned to robots based on capabilities such as traversability or perception and refined given feedback collected during online operation. 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\% 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.
関連論文
- 異種ロボット協調のための再帰的自己改善エージェントOS異種ロボット協調
- 地下鉱山における異種ロボット協調のための空間認識異種ロボット協調
