Humans and animals are capable of learning a new behavior by observing others perform
the skill just once. We consider the problem of allowing a robot to do the same--learning …

Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex
behaviors through experience. However, realizing this promise for long-horizon tasks in the …

Humans have a remarkable ability to learn new concepts from only a few examples and
quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience …

Prediction is believed to play an important role in the human brain. However, it is still unclear
how predictions are used in the process of learning new movements. In this paper, we …

We present O2A, a novel method for learning to perform robotic manipulation tasks from a
single (one-shot) third-person demonstration video. To our knowledge, it is the first time this …

Learning new tasks has always been a challenging problem in robotics. Even though
several approaches have been proposed, from manual programming to learning from …

The goal of Learning from Demonstration is to automatically transfer the skill knowledge
from human to robot. Current researches focus on the problem of modeling …

Distribution shift in machine learning refers to the general problem where a model is
evaluated on test data drawn from a different distribution than the training data distribution …