Name
Abstract | ||
|---|---|---|
This paper presents a framework to learn the sequential structure in the demonstrations for robot imitation learning. We first present a family of task-parameterized hidden semi-Markov models that extracts invariant segments (also called sub-goals or options) from demonstrated trajectories, and optimally follows the sampled sequence of states from the model with a linear quadratic tracking controller. We then extend the concept to learning invariant segments from visual observations that are sequenced together for robot imitation. We present Motion2Vec that learns a deep embedding space by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while being pushed away from randomly sampled images of other segments, and a time contrastive loss is used to preserve the temporal ordering of the images. The trained embeddings are segmented with a recurrent neural network, and subsequently used for decoding the end-effector pose of the robot. We first show its application to a pick-and-place task with the Baxter robot while avoiding a moving obstacle from four kinesthetic demonstrations only, followed by suturing task imitation from publicly available suturing videos of the JIGSAWS dataset with state-of-the-art 85 . 5 % segmentation accuracy and 0 . 94 cm error in position per observation on the test set. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1177/02783649211032721 | INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH |
Keywords | DocType | Volume |
Hidden semi-Markov model, robot learning, imitation learning, learning and adaptive systems | Journal | 40 |
Issue | ISSN | Citations |
10-11 | 0278-3649 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ajay Kumar Tanwani | 1 | 66 | 9.07 |
| Andy Yan | 2 | 1 | 0.70 |
| Jonathan Lee | 3 | 70 | 16.21 |
| Sylvain Calinon | 4 | 1897 | 117.63 |
| Ken Goldberg | 5 | 3785 | 369.80 |