Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery

The upper boundary of time delay is often required in traditional telesurgery control design, which would result in infeasibility of telesurgery across regions. To overcome this issue, this paper introduces a new control framework based on deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithm. The developed framework effectively overcomes the phase difference and data loss caused by time delays, which facilitates the restoration of surgeon’s intention and interactive force. Kalman filter (KF) is employed to blend multiple surgeons’ commands and predict the final local commands, respectively. The control framework ensures synchronization tracking performance and transparency. Prior knowledge of time delay is therefore not required. Simulation and experiment results have demonstrated the merits of the proposed framework

Anthropomorphic Dual-Arm Coordinated Control for a Single-Port Surgical Robot Based on Dual-Step Optimization

Effective teleoperation of the small-scale and highly-integrated robots for single-port surgery (SPS) imposes unique control and human-robot interaction challenges. Traditional isometric teleoperation schemes mainly focus on end-to-end trajectory mapping, which is problematic when applied to SPS robotic control, especially for dual-arm coordinated operation. Inspired by the human arm configuration in boxing maneuvers, an optimized anthropomorphic coordinated control strategy based on a dual-step optimization approach is proposed. Theoretical derivation and solvability of the problem are addressed, and the effectiveness of the method is further demonstrated in detailed simulation and in-vitro experiments. The proposed control strategy has been shown to perform dexterous SPS bimanual manipulation more effectively, involving less instrument-interference and is free from singularities, thereby improving the safety and efficiency of SPS operations

Differentiation and characterization of rhesus monkey atrial and ventricular cardiomyocytes from induced pluripotent stem cells

The combination of non-human primate animals and their induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) provides not only transplantation models for cell-based therapy of heart diseases, but also opportunities for heart-related drug research on both cellular and animal levels. However, the subtypes and electrophysiology properties of non-human primate iPSC-CMs hadn't been detailed characterized. In this study, we generated rhesus monkey induced pluripotent stem cells (riPSCs), and efficiently differentiated them into ventricular or atrial cardiomyocytes by modulating retinoic acid (RA) pathways. Our results revealed that the electrophysiological characteristics and response to canonical drugs of riPSC-CMs were similar with those of human pluripotent stem cell derived CMs. Therefore, rhesus monkeys and their iPSC-CMs provide a powerful and practicable system for heart related biomedical research