未来予測による言語からの解釈可能な計画生成
Prospection: Interpretable Plans From Language By Predicting the Future
自然言語の指示を中間目標の系列に変換する枠組みを提案し、行動前に結果を予測する「プロスペクション」を学習させることで、シミュレーション環境でのロボット指示解釈の精度を示した。
著者: Chris Paxton, Yonatan Bisk, Jesse Thomason, Arunkumar Byravan, Dieter Fox
分類: cs.AI, cs.CL, cs.LG, cs.RO
原文アブストラクト
High-level human instructions often correspond to behaviors with multiple implicit steps. In order for robots to be useful in the real world, they must be able to to reason over both motions and intermediate goals implied by human instructions. In this work, we propose a framework for learning representations that convert from a natural-language command to a sequence of intermediate goals for execution on a robot. A key feature of this framework is prospection, training an agent not just to correctly execute the prescribed command, but to predict a horizon of consequences of an action before taking it. We demonstrate the fidelity of plans generated by our framework when interpreting real, crowd-sourced natural language commands for a robot in simulated scenes.
