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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
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 …
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
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 …
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …
Interactive imitation learning in robotics: A survey
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 …
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …
Agile autonomous driving using end-to-end deep imitation learning
Hg-dagger: Interactive imitation learning with human experts
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 …
such as behavioral cloning suffer from data mismatch and compounding error issues. One …
Dart: Noise injection for robust imitation learning
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 …
a supervisor and infers a control policy. A known problem with this “off-policy" approach is …
Imitation learning for agile autonomous driving
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 …
using only low-cost on-board sensors. By imitating a model predictive controller equipped …
Imitation learning as f-divergence minimization
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 …
attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of …