  While robot learning has demonstrated promising results for enabling robots
to automatically acquire new skills, a critical challenge in deploying
learning-based systems is scale: acquiring enough data for the robot to
effectively generalize broadly. Imitation learning, in particular, has remained
a stable and powerful approach for robot learning, but critically relies on
expert operators for data collection. In this work, we target this challenge,
aiming to build an imitation learning system that can continuously improve
through autonomous data collection, while simultaneously avoiding the explicit
use of reinforcement learning, to maintain the stability, simplicity, and
scalability of supervised imitation. To accomplish this, we cast the problem of
imitation with autonomous improvement into a multi-task setting. We utilize the
insight that, in a multi-task setting, a failed attempt at one task might
represent a successful attempt at another task. This allows us to leverage the
robot's own trials as demonstrations for tasks other than the one that the
robot actually attempted. Using an initial dataset of multi-task demonstration
data, the robot autonomously collects trials which are only sparsely labeled
with a binary indication of whether the trial accomplished any useful task or
not. We then embed the trials into a learned latent space of tasks, trained
using only the initial demonstration dataset, to draw similarities between
various trials, enabling the robot to achieve one-shot generalization to new
tasks. In contrast to prior imitation learning approaches, our method can
autonomously collect data with sparse supervision for continuous improvement,
and in contrast to reinforcement learning algorithms, our method can
effectively improve from sparse, task-agnostic reward signals.
