Imitation learning holds tremendous promise in learning policies efficiently for complex
decision making problems. Current state-of-the-art algorithms often use inverse …

In recent years, increasing attention has been directed to leveraging pre-trained vision
models for motor control. While existing works mainly emphasize the importance of this pre …

Pre-trained text-to-image generative models can produce diverse, semantically rich, and
realistic images from natural language descriptions. Compared with language, images …

Cross-domain imitation learning studies how to leverage expert demonstrations of one
agent to train an imitation agent with a different embodiment or morphology. Comparing …

Adversarial imitation learning (AIL) is a popular method that has recently achieved much
success. However, the performance of AIL is still unsatisfactory on the more challenging …

When operating in service of people, robots need to optimize rewards aligned with end-user
preferences. Since robots will rely on raw perceptual inputs like RGB images, their rewards …

Humans often acquire new skills through observation and imitation. For robotic agents,
learning from the plethora of unlabeled video demonstration data available on the Internet …

Most Reinforcement Learning (RL) methods are traditionally studied in an active learning
setting, where agents directly interact with their environments, observe action outcomes, and …

We study imitation learning using only visual observations for controlling dynamical systems
with continuous states and actions. This setting is attractive due to the large amount of video …

Imitation learning (IL) has achieved considerable success in solving complex sequential
decision-making problems. However, current IL methods mainly assume that the …