… robot. In this paper, we survey the progress in model learning with a strong focus on robot
control … Here, a model describes essential information about the behavior of the environment …

… for sample-efficient robot learning, we apply the algorithm to learn robot locomotion,
manipulation, and navigation tasks from scratch in the real world on 4 robots, without simulators. …

… training dataset during model learning. Then, in order to merge the high asymptotic performance
of the model-free algorithm, we use the DDPG algorithm to optimize robot control policy. …

… Learning of internal models for robot control has found increasingly more widespread
application due to significant advances in the computational efficiency of supervised learning …

… mobile robots. We present a model-based reinforcement learning approach for robots to …
from a deep transition model that predicts the evolution of surrounding dynamics of mobile …

… The purpose of this article is to survey such existing micro-data policy search (MDPS)
techniques that have been successfully used for robot control,2 and to identify the challenges in …

… approaches require iteratively obtaining millions of robot samples to learn a policy, which …
learning a realistic world model capturing the dynamics of scene changes conditioned on robot …

… Most common robot control tasks such as manipulation or navigation tasks require a robot
… As a result, in this paper, we focus robot control policy learning on different forms of reaching …

… Thus, more improvements are needed to reach our goal of real-life learning for multi-robot
systems. One development would be to improve model learning speed and quality (for …

… While it motivates us to learn world models on top of MAE representations, we find that MAE
… robot control tasks from Meta-world [16], RLBench [17], and DeepMind Control Suite [41]. …