  Shared autonomy is an operational concept in which a user and an autonomous
agent collaboratively control a robotic system. It provides a number of
advantages over the extremes of full-teleoperation and full-autonomy in many
settings. Traditional approaches to shared autonomy rely on knowledge of the
environment dynamics, a discrete space of user goals that is known a priori, or
knowledge of the user's policy -- assumptions that are unrealistic in many
domains. Recent works relax some of these assumptions by formulating shared
autonomy with model-free deep reinforcement learning (RL). In particular, they
no longer need knowledge of the goal space (e.g., that the goals are discrete
or constrained) or environment dynamics. However, they need knowledge of a
task-specific reward function to train the policy. Unfortunately, such reward
specification can be a difficult and brittle process. On top of that, the
formulations inherently rely on human-in-the-loop training, and that
necessitates them to prepare a policy that mimics users' behavior. In this
paper, we present a new approach to shared autonomy that employs a modulation
of the forward and reverse diffusion process of diffusion models. Our approach
does not assume known environment dynamics or the space of user goals, and in
contrast to previous work, it does not require any reward feedback, nor does it
require access to the user's policy during training. Instead, our framework
learns a distribution over a space of desired behaviors. It then employs a
diffusion model to translate the user's actions to a sample from this
distribution. Crucially, we show that it is possible to carry out this process
in a manner that preserves the user's control authority. We evaluate our
framework on a series of challenging continuous control tasks, and analyze its
ability to effectively correct user actions while maintaining their autonomy.
