Continual Learning on Incremental Simulations for Real-World Robotic Manipulation Tasks

Abstract

Current state-of-the-art approaches for transferring deep-learning models trained in simulation either rely on highly realistic simulations or employ randomization techniques to bridge the reality gap. However, such strategies do not scale well for complex robotic tasks; highly-realistic simulations are computationally expensive and hard to implement, while randomization techniques become sample-inefficient as the complexity of the task increases. In this paper, we propose a procedure for training on incremental simulations in a continual learning setup. We analyze whether such setup can help to reduce the training time for complex tasks and improve the sim2real transfer. For the experimental analysis, we develop a simulation platform that can serve as a training environment and as a benchmark for continual and reinforcement learning sim2real approaches