ProgPrompt: 大規模言語モデルを用いた状況依存ロボットタスクプランの生成
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
環境内の利用可能な行動と物体をプログラム風に記述し、実行可能な例示プログラムと共にLLMへ入力することで、状況に応じたロボットのタスクプランを生成する手法を提案。
著者: Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, Animesh Garg
分類: cs.RO, cs.AI, cs.CL, cs.LG
原文アブストラクト
Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information. However, such methods either require enumerating all possible next steps for scoring, or generate free-form text that may contain actions not possible on a given robot in its current context. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments, robot capabilities, and tasks. Our key insight is to prompt the LLM with program-like specifications of the available actions and objects in an environment, as well as with example programs that can be executed. We make concrete recommendations about prompt structure and generation constraints through ablation experiments, demonstrate state of the art success rates in VirtualHome household tasks, and deploy our method on a physical robot arm for tabletop tasks. Website at progprompt.github.io
