  While deep reinforcement learning (RL) agents have demonstrated incredible
potential in attaining dexterous behaviours for robotics, they tend to make
errors when deployed in the real world due to mismatches between the training
and execution environments. In contrast, the classical robotics community have
developed a range of controllers that can safely operate across most states in
the real world given their explicit derivation. These controllers however lack
the dexterity required for complex tasks given limitations in analytical
modelling and approximations. In this paper, we propose Bayesian Controller
Fusion (BCF), a novel uncertainty-aware deployment strategy that combines the
strengths of deep RL policies and traditional handcrafted controllers. In this
framework, we can perform zero-shot sim-to-real transfer, where our uncertainty
based formulation allows the robot to reliably act within out-of-distribution
states by leveraging the handcrafted controller while gaining the dexterity of
the learned system otherwise. We show promising results on two real-world
continuous control tasks, where BCF outperforms both the standalone policy and
controller, surpassing what either can achieve independently. A supplementary
video demonstrating our system is provided at https://bit.ly/bcf_deploy.

Robotics;Artificial Intelligence