Name
Title | ||
|---|---|---|
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space |
Abstract | ||
|---|---|---|
Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination approach which, unlike previous approaches, takes into account the reliability of the learned dynamics model used for imagining the future. Our approach learns an ensemble of disjoint local dynamics models in latent space and derives an intrinsic reward based on learning progress, motivating the controller to take actions leading to data that improves the models. The learned models are used to generate imagined experiences, augmenting the training set of real experiences. We evaluate our approach on learning vision-based robotic grasping and show that it significantly improves sample efficiency and achieves near-optimal performance in a sparse reward environment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DEVLRN.2019.8850723 | 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) |
Keywords | Field | DocType |
vision-based robotic grasping,model-free deep reinforcement learning,complex control tasks,human mental simulation,motor behavior,learning-adaptive imagination approach,disjoint local dynamics models,model-based deep reinforcement learning | Training set,Control theory,Disjoint sets,Motor behavior,Artificial intelligence,Machine learning,Imagination,Mathematics,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
2161-9484 | 978-1-5386-8129-9 | 1 |
PageRank | References | Authors |
0.35 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Burhan Hafez | 1 | 13 | 3.59 |
| Cornelius Weber | 2 | 318 | 41.92 |
| Matthias Kerzel | 3 | 32 | 7.67 |
| Stefan Wermter | 4 | 1100 | 151.62 |