SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Control
By Arunkumar Byravan, Felix Leeb, F. Meier, Dieter Fox. 2018
37 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Sergey Levine
- Chelsea Finn
- Mrinal Kalakrishnan
- Julian Ibarz
- Pieter Abbeel
- Byron Boots
- Peter Pastor
- Jeannette Bohg
In Computer Vision and Pattern Recognition
- Sergey Levine
- Chelsea Finn
- Dacheng Tao
- Jitendra Malik
- Luc Van Gool
- Trevor Darrell
- Abhinav Kumar Gupta
- Andrew Zisserman
Citations in the corpus
- Differentiable Gaussian Process Motion Planning (2019, methodology): “[16] learns structured dynamics models for reactive visuomotor control.”
- Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey (2020, methodology): “ representation Active self ability Learning method Watter et al. (2015) V implicit VAE Agency Unsupervised representation learning Van Hoof et al. (2016) T implicit VAE Agency Reinforcement learning Byravan et al. (2018) V (3D point cloud) explicit SE3-POSE-NETS Agency Unsupervised representation l”
- Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects (2019, methodology): “[11] train networks that learn robot’s latent link segmentation and pose space dynamics from point cloud time series, using a reconstruction loss for self-supervision.”
- How to train your robot with deep reinforcement learning: lessons we have learned (2021, methodology): “Further, it has been used for visual reaching tasks (Byravan et al., 2018), object pushing and trajectory following tasks (Yen-Chen et al.”