LLMチェイニングによる汎用サービスロボットのタスクプランニングの設計と評価
Design and Evaluation of LLM Chaining-Based Task Planning for General Purpose Service Robots
LLMを命令分類と行動生成の2段階に分けるチェイニング構成で、プロンプト長を約45%削減しつつタスクプランニングの一貫性を向上させ、実機HSRで評価した。
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著者: Lucas Da Mota Bruno, Jiahao Sim, Yoshinobu Hagiwara
分類: cs.RO, cs.AI, cs.CL
原文アブストラクト
General Purpose Service Robot (GPSR) tasks, as defined in the RoboCup@Home benchmark, require robots to interpret diverse natural language commands and generate multi-step action sequences in real home environments. Conventional Single Prompt (SP) approaches suffer from context bloat and the "Lost in the Middle" phenomenon, leading to unreliable task planning. We propose an LLM chaining architecture that separates instruction classification and action generation into two specialized stages, reducing per-inference prompt length by approximately 45% while improving planning consistency. We evaluate our method using 100 randomly generated GPSR commands across three language models spanning local open-source and frontier cloud deployment contexts. Results show consistent planning improvements over SP across all models, with gains of up to +37 percentage points on local models. Further, real-robot execution experiments on the Toyota Human Support Robot (HSR) reveal that planning success alone does not guarantee task completion, with 6 of 10 tasks completing successfully and execution-layer failures identified as the primary remaining bottleneck.