言語で操作を変調する潜在アクション制御
LILA: Language-Informed Latent Actions
言語指示と低次元コントローラを組み合わせ、言語に応じて操作空間を変調する共有自律の枠組みLILAを提案し、実機ユーザー実験で有効性を示した。
著者: Siddharth Karamcheti, Megha Srivastava, Percy Liang, Dorsa Sadigh
分類: cs.RO, cs.AI, cs.CL, cs.HC, cs.LG
原文アブストラクト
We introduce Language-Informed Latent Actions (LILA), a framework for learning natural language interfaces in the context of human-robot collaboration. LILA falls under the shared autonomy paradigm: in addition to providing discrete language inputs, humans are given a low-dimensional controller $-$ e.g., a 2 degree-of-freedom (DoF) joystick that can move left/right and up/down $-$ for operating the robot. LILA learns to use language to modulate this controller, providing users with a language-informed control space: given an instruction like "place the cereal bowl on the tray," LILA may learn a 2-DoF space where one dimension controls the distance from the robot's end-effector to the bowl, and the other dimension controls the robot's end-effector pose relative to the grasp point on the bowl. We evaluate LILA with real-world user studies, where users can provide a language instruction while operating a 7-DoF Franka Emika Panda Arm to complete a series of complex manipulation tasks. We show that LILA models are not only more sample efficient and performant than imitation learning and end-effector control baselines, but that they are also qualitatively preferred by users.
