Controlling Assistive Robots with Learned Latent Actions
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Assistive robots enable users with disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms useful also makes them challenging to control: the robot has more degrees-of-freedom (DoFs) than the human can directly coordinate with a handheld joystick. Our insight is that we can make assistive robots easier for humans to control by learning latent actions. Latent actions provide a low-DoF embedding of high-DoF robot behavior: for example, one latent dimension might guide the robot arm along a pouring motion. Because these latent actions are low-dimensional, they can be controlled by the human end-user to fluidly teleoperate the robot. In this paper, we design a teleoperation algorithm for assistive robots that learns intuitive latent dimensions from task demonstrations. We formulate the controllability, consistency, and scaling properties that intuitive latent actions should have, and evaluate how different low-dimensional embeddings capture these properties. Finally, we conduct two user studies on a robotic arm to compare our latent action approach to state-of-the-art shared autonomy baselines and direct end-effector teleoperation. Participants completed the assistive feeding and cooking tasks more quickly and efficiently when leveraging our latent actions, and also reported that latent actions made the task easier to perform.
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