Our goal is to train a policy for autonomous driving via imitation learning that is robust
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …

To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …

We present an end-to-end imitation learning system for agile, off-road autonomous driving
using only low-cost sensors. By imitating a model predictive controller equipped with …

Imitation Learning has empowered recent advances in learning robotic manipulation tasks
by addressing shortcomings of Reinforcement Learning such as exploration and reward …

Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires
reasoning about the desired effect of the task and, thus, properly grasping and manipulating …

We present an end-to-end imitation learning system for agile, off-road autonomous driving
using only low-cost on-board sensors. By imitating a model predictive controller equipped …

Abstract One approach to Imitation Learning is Behavior Cloning, in which a robot observes
a supervisor and infers a control policy. A known problem with this “off-policy" approach is …

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 address the problem of imitation learning with multi-modal demonstrations. Instead of
attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of …