Efficient online adaptation with stochastic recurrent neural networks

Abstract

Autonomous robots need to interact with unknown and unstructured environments. For continuous online adaptation in lifelong learning scenarios, they need sample-efficient mechanisms to adapt to changing environments, constraints, tasks and capabilities. In this paper, we introduce a framework for online motion planning and adaptation based on a bio-inspired stochastic recurrent neural network. By using the intrinsic motivation signal cognitive dissonance with a mental replay strategy, the robot can learn from few physical interactions and can therefore adapt to novel environments in seconds. We evaluate our online planning and adaptation framework on a KUKA LWR arm. The efficient online adaptation is shown by learning unknown workspace constraints sample-efficient within few seconds while following given via points.