End-effector based assistive robots face persistent challenges in generating smooth and robust trajectories when controlled by human's noisy and unreliable biosignals such as muscle activities and brainwaves. The produced endpoint trajectories are often jerky and imprecise to perform complex tasks such as stable robotic grasping. We propose STREAMS (Self-Training Robotic End-to-end Adaptive Multimodal Shared autonomy) as a novel framework leveraged deep reinforcement learning to tackle this challenge in biosignal based robotic control systems. STREAMS blends environmental information and synthetic user input into a Deep Q Learning Network (DQN) pipeline for an interactive end-to-end and self-training mechanism to produce smooth trajectories for the control of end-effector based robots. The proposed framework achieved a high-performance record of 98% in simulation with dynamic target estimation and acquisition without any pre-existing datasets. As a zero-shot sim-to-real user study with five participants controlling a physical robotic arm with noisy head movements, STREAMS (as an assistive mode) demonstrated significant improvements in trajectory stabilization, user satisfaction, and task performance reported as a success rate of 83% compared to manual mode which was 44% without any task support. STREAMS seeks to improve biosignal based assistive robotic controls by offering an interactive, end-to-end solution that stabilizes end-effector trajectories, enhancing task performance and accuracy.

翻译：基于末端执行器的辅助机器人在由人类嘈杂且不可靠的生物信号（如肌肉活动和脑电波）控制时，面临生成平滑且鲁棒轨迹的持续挑战。所产生的末端轨迹通常不稳定且不精确，难以执行诸如稳定机器人抓取等复杂任务。我们提出STREAMS（自训练机器人端到端自适应多模态共享自主性）作为一种新颖框架，利用深度强化学习来应对生物信号机器人控制系统中的这一挑战。STREAMS将环境信息和合成用户输入融合到深度Q学习网络（DQN）流程中，通过交互式端到端自训练机制，为基于末端执行器的机器人控制生成平滑轨迹。所提出的框架在动态目标估计与获取的仿真中取得了98%的高性能记录，且无需任何预先存在的数据集。在一项涉及五名参与者使用嘈杂头部运动控制物理机械臂的零样本仿真到现实用户研究中，STREAMS（作为辅助模式）在轨迹稳定性、用户满意度和任务性能方面均展现出显著提升，其报告的成功率为83%，而没有任何任务支持的手动模式成功率仅为44%。STREAMS旨在通过提供一种交互式、端到端的解决方案来改善基于生物信号的辅助机器人控制，该方案能稳定末端执行器轨迹，从而提升任务性能与准确性。