  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.
