  Recent advances in deep reinforcement learning (RL) have demonstrated its
potential to learn complex robotic manipulation tasks. However, RL still
requires the robot to collect a large amount of real-world experience. To
address this problem, recent works have proposed learning from expert
demonstrations (LfD), particularly via inverse reinforcement learning (IRL),
given its ability to achieve robust performance with only a small number of
expert demonstrations. Nevertheless, deploying IRL on real robots is still
challenging due to the large number of robot experiences it requires. This
paper aims to address this scalability challenge with a robust,
sample-efficient, and general meta-IRL algorithm, SQUIRL, that performs a new
but related long-horizon task robustly given only a single video demonstration.
First, this algorithm bootstraps the learning of a task encoder and a
task-conditioned policy using behavioral cloning (BC). It then collects
real-robot experiences and bypasses reward learning by directly recovering a
Q-function from the combined robot and expert trajectories. Next, this
algorithm uses the Q-function to re-evaluate all cumulative experiences
collected by the robot to improve the policy quickly. In the end, the policy
performs more robustly (90%+ success) than BC on new tasks while requiring no
trial-and-errors at test time. Finally, our real-robot and simulated
experiments demonstrate our algorithm's generality across different state
spaces, action spaces, and vision-based manipulation tasks, e.g.,
pick-pour-place and pick-carry-drop.
