Name
Abstract | ||
|---|---|---|
Humans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data. Videos can be found here: https://sites.google.com/view/tecnets-humans. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LRA.2020.2977835 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Learning from demonstration, deep learning in robotics and automation, perception for grasping and manipulation | Journal | 5 |
Issue | ISSN | Citations |
2 | 2377-3766 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Bonardi | 1 | 1 | 0.35 |
| Stephen James | 2 | 58 | 6.02 |
| Andrew J. Davison | 3 | 6707 | 350.85 |