Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
Rituraj Kaushik,Timothée Anne,Jean-Baptiste Mouret,Rituraj Kaushik,Timothée Anne,Jean-Baptiste Mouret
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points. However, in the real world, a robot might encounter any situation starting from motor failures to finding itself in a rocky terrain ...
