@xf1280 I'm fascinated by your research. Would it be possible for you to share the simulation environment for dexterous manipulator robot? I believe it would significantly benefit my research.
Deep RL agents are data hungry and often learn task-specific representations. Our model learns object-centric abstractions from raw videos. This enables highly data-efficient RL and structured exploration. https://t.co/AnYkOYKRTO
.@Kaplanyan has been killing it with these foveated reconstruction papers. Absolutely amazing that this is possible with so little data. Have a look! - https://t.co/pl7nHbDpkn
Dataset: 64 minutes of challenging pedestrian areas captured with:
- stereo cylindrical 360 RGB cam
- 3D point clouds from two Veloydyne 16 Lidars
- line 3Dpoint clouds from two Sick Lidars
- Audio, RGBD, 360 spherical image from a fisheye camera
https://t.co/5zYzwwpRBM
We’ve developed one of the first systems that uses only 3D point clouds and achieves higher precision in detecting 3D objects than prior work. Read more: https://t.co/64RCwWsrcN. And we’ve open-sourced this model here: https://t.co/fJl0hXR5nq #ICCV2019