  Imitation learning holds tremendous promise in learning policies efficiently
for complex decision making problems. Current state-of-the-art algorithms often
use inverse reinforcement learning (IRL), where given a set of expert
demonstrations, an agent alternatively infers a reward function and the
associated optimal policy. However, such IRL approaches often require
substantial online interactions for complex control problems. In this work, we
present Regularized Optimal Transport (ROT), a new imitation learning algorithm
that builds on recent advances in optimal transport based trajectory-matching.
Our key technical insight is that adaptively combining trajectory-matching
rewards with behavior cloning can significantly accelerate imitation even with
only a few demonstrations. Our experiments on 20 visual control tasks across
the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World
Benchmark demonstrate an average of 7.8X faster imitation to reach 90% of
expert performance compared to prior state-of-the-art methods. On real-world
robotic manipulation, with just one demonstration and an hour of online
training, ROT achieves an average success rate of 90.1% across 14 tasks.
