Multiple-Pilot Collaboration for Advanced Remote Intervention using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based co-pilot framework for cooperative teleoperation that reduces workload and improves robustness without prior delay knowledge, validated through experiments.

Contribution

It proposes a novel multi-pilot control framework using DDPG and fuzzy identification to enhance teleoperation robustness and force feedback without delay.

Findings

Effective command restoration via DDPG-based arbitration

Improved force feedback reconstruction without delay

Validated through two experimental applications

Abstract

The traditional master-slave teleoperation relies on human expertise without correction mechanisms, resulting in excessive physical and mental workloads. To address these issues, a co-pilot-in-the-loop control framework is investigated for cooperative teleoperation. A deep deterministic policy gradient(DDPG) based agent is realised to effectively restore the master operators' intents without prior knowledge on time delay. The proposed framework allows for introducing an operator (i.e., co-pilot) to generate commands at the slave side, whose weights are optimally assigned online through DDPG-based arbitration, thereby enhancing the command robustness in the case of possible human operational errors. With the help of interval type-2(IT2) Takagi-Sugeno (T-S) fuzzy identification, force feedback can be reconstructed at the master side without a sense of delay, thus ensuring the telepresence performance in the force-sensor-free scenarios. Two experimental applications validate the effectiveness of the proposed framework.