Name
Title | ||
|---|---|---|
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger. |
Abstract | ||
|---|---|---|
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator. We show empirical benefits, both in simulation and sim-to-real transfer, of using keypoints as opposed to position+quaternion representations for the object pose in 6-DoF for policy observations and in reward calculation to train a model-free reinforcement learning agent. By utilizing domain randomization strategies along with the keypoint representation of the pose of the manipulated object, we achieve a high success rate of 83% on a remote TriFinger system maintained by the organizers of the Real Robot Challenge. With the aim of assisting further research in learning in-hand manipulation, we make the codebase of our system, along with trained checkpoints that come with billions of steps of experience available, at https://s2r2-ig.github.io |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/IROS47612.2022.9981458 | IEEE/RJS International Conference on Intelligent RObots and Systems (IROS) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arthur Allshire | 1 | 0 | 0.68 |
| Mayank Mittal | 2 | 2 | 2.05 |
| Varun Lodaya | 3 | 0 | 0.34 |
| Viktor Makoviychuk | 4 | 2 | 1.74 |
| Denys Makoviichuk | 5 | 0 | 0.34 |
| Felix Widmaier | 6 | 0 | 1.35 |
| Manuel Wuethrich | 7 | 13 | 5.30 |
| Stefan Bauer | 8 | 0 | 0.34 |
| Ankur Handa | 9 | 479 | 26.11 |
| Animesh Garg | 10 | 0 | 0.68 |