Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert
demonstrations. However, policies learned through IL suffer from state distribution shift at …

Imitation learning offers a promising path for robots to learn general-purpose behaviors, but
traditionally has exhibited limited scalability due to high data supervision requirements and …

Imitation learning is an effective and safe technique to train robot policies in the real world
because it does not depend on an expensive random exploration process. However, due to …

Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations,
or via reinforcement learning (RL) using large amountsof autonomously collected …

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 …

In recent years, we have seen an emergence of data-driven approaches in robotics.
However, most existing efforts and datasets are either in simulation or focus on a single task …

Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

Imitation learning has proven to be useful for many real-world problems, but approaches
such as behavioral cloning suffer from data mismatch and compounding error issues. One …

We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel
imitation learning algorithm that matches the state-action distribution to the distribution …