身体制御タスクの指導のための言語修正生成
Generating Language Corrections for Teaching Physical Control Tasks
学生と専門家の軌道ペアから自然言語の修正フィードバックを生成するモデルCORGIを構築し、描画・操舵・関節運動の3タスクで有効性を検証した。
著者: Megha Srivastava, Noah Goodman, Dorsa Sadigh
分類: cs.AI, cs.CL, cs.HC, cs.RO
原文アブストラクト
AI assistance continues to help advance applications in education, from language learning to intelligent tutoring systems, yet current methods for providing students feedback are still quite limited. Most automatic feedback systems either provide binary correctness feedback, which may not help a student understand how to improve, or require hand-coding feedback templates, which may not generalize to new domains. This can be particularly challenging for physical control tasks, where the rich diversity in student behavior and specialized domains make it challenging to leverage general-purpose assistive tools for providing feedback. We design and build CORGI, a model trained to generate language corrections for physical control tasks, such as learning to ride a bike. CORGI takes in as input a pair of student and expert trajectories, and then generates natural language corrections to help the student improve. We collect and train CORGI over data from three diverse physical control tasks (drawing, steering, and joint movement). Through both automatic and human evaluations, we show that CORGI can (i) generate valid feedback for novel student trajectories, (ii) outperform baselines on domains with novel control dynamics, and (iii) improve student learning in an interactive drawing task.
