Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: SE 3-Pose-Nets : Structured Deep Dynamics Models for Visuomotor Planning and Control
SE3-nets: Learning rigid body motion using deep neural networks
Deep spatial autoencoders for visuomotor learning
Recurrent 3D Pose Sequence Machines
Predicting Object Dynamics in Scenes
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation
Socially aware motion planning with deep reinforcement learning
Multiple camera model-based 3-D visual servo
Learning Latent Dynamics for Planning from Pixels
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
Gaussian Process Dynamical Models for Human Motion
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
Adaptive Path-Integral Approach to Representation Learning and Planning for Dynamical Systems
Learning 3D Human Dynamics From Video
DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning
QMDP-Net: Deep Learning for Planning under Partial Observability
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
towards adapting deep visuomotor representations from simulated to real environments .
An enterprise architecture planning for higher education using the open group architecture framework (togaf): Case study University of Lampung
PatchBatch: A Batch Augmented Loss for Optical Flow
Omnidirectional Mobile Robot - Design and Implementation
Jacobi spectral collocation method for the approximate solution of multidimensional nonlinear Volterra integral equation
sensor information networking architecture and applications .