LILA: Language-Informed Latent Actions

• URL: http://arxiv.org/abs/2111.03205v1
• Date: Fri, 5 Nov 2021 00:56:00 GMT
• Title: LILA: Language-Informed Latent Actions
• Authors: Siddharth Karamcheti, Megha Srivastava, Percy Liang, Dorsa Sadigh
• Abstract summary: We introduce Language-Informed Latent Actions (LILA), a framework for learning natural language interfaces in the context of human-robot collaboration. LILA learns to use language to modulate a low-dimensional controller, providing users with a language-informed control space. 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.
• Score: 72.033770901278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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