  We can make it easier for disabled users to control assistive robots by
mapping the user's low-dimensional joystick inputs to high-dimensional, complex
actions. Prior works learn these mappings from human demonstrations: a
non-disabled human either teleoperates or kinesthetically guides the robot arm
through a variety of motions, and the robot learns to reproduce the
demonstrated behaviors. But this framework is often impractical - disabled
users will not always have access to external demonstrations! Here we instead
learn diverse teleoperation mappings without either human demonstrations or
pre-defined tasks. Under our unsupervised approach the robot first optimizes
for object state entropy: i.e., the robot autonomously learns to push, pull,
open, close, or otherwise change the state of nearby objects. We then embed
these diverse, object-oriented behaviors into a latent space for real-time
control: now pressing the joystick causes the robot to perform dexterous
motions like pushing or opening. We experimentally show that - with a best-case
human operator - our unsupervised approach actually outperforms the
teleoperation mappings learned from human demonstrations, particularly if those
demonstrations are noisy or imperfect. But our user study results were less
clear-cut: although our approach enabled participants to complete tasks more
quickly and with fewer changes of direction, users were confused when the
unsupervised robot learned unexpected behaviors. See videos of the user study
here: https://youtu.be/BkqHQjsUKDg
