Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics

Understand

We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learning (RL).

• BCF thrives in the robotics domain, where reliable but suboptimal control priors exist for many tasks, but RL from scratch remains unsafe and data-inefficient.
• By fusing uncertainty-aware distributional outputs from each system, BCF arbitrates control between them, exploiting their respective strengths.
• We study BCF on two real-world robotics tasks involving navigation in a vast and long-horizon environment, and a complex reaching task that involves manipulability maximisation.