Error-related potential-based shared autonomy via deep recurrent reinforcement learning

Abstract

Objective. Error-related potential (ErrP)-based brain–computer interfaces (BCIs) have received a considerable amount of attention in the human–robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human–robot interaction. Approach. We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users. Main results. The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster. Significance. The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human–robot interaction task.