Name
Title | ||
|---|---|---|
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation |
Abstract | ||
|---|---|---|
We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actorcritic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what part of the scene to ‘zoom’ into. When this ‘zooming’ behaviour is applied iteratively, it results in a near-lossless discretisation of the translation space, and allows the use of a discrete action, deep Q-learning method. We show that our new coarse-to-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.01337 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Vision applications and systems, Others, Robot vision | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen James | 1 | 58 | 6.02 |
| Kentaro Wada | 2 | 0 | 0.34 |
| Tristan Laidlow | 3 | 8 | 2.20 |
| Andrew J. Davison | 4 | 6707 | 350.85 |