Imitation learning (IL) algorithms excel in acquiring high-quality policies from expert data for
sequential decision-making tasks. But, their effectiveness is hampered when faced with …

Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

Offline reinforcement learning (RL) presents a promising approach for learning reinforced
policies from offline datasets without the need for costly or unsafe interactions with the …

Adversarial imitation learning has become a widely used imitation learning framework. The
discriminator is often trained by taking expert demonstrations and policy trajectories as …

Learning tasks only from raw video demonstrations is the current state of the art in robotics
visual imitation learning research. The implicit assumption here is that all video …

This research is about learning high-level policies of multi-step sequential (MSS) tasks–such
as activities of daily living–from demonstrations in a sample efficient manner. This research …

Imitation learning (IL) is a machine learning technique that involves learning from examples
provided by an expert. IL algorithms can solve the sequential decision-making tasks but their …