STREAMS：一个用于赋能基于生物信号机器人控制的辅助多模态 AI 框架
STREAMS: An Assistive Multimodal AI Framework for Empowering Biosignal Based Robotic Controls
AI总结：
本文提出 STREAMS 框架，融合环境信息与合成生物信号输入，通过基于 DQN 的深度强化学习和自训练共享自主机制生成平滑机器人轨迹，并在仿真与零样本真机用户研究中验证其显著提升。
摘要：
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，在生成平滑且鲁棒的轨迹方面面临持续挑战。所产生的末端轨迹通常抖动且不精确，难以完成稳定机器人抓取等复杂任务。我们提出 STREAMS（Self-Training Robotic End-to-end Adaptive Multimodal Shared autonomy，自训练机器人端到端自适应多模态共享自主），这是一种利用深度强化学习来应对基于生物信号的机器人控制系统中这一挑战的新颖框架。STREAMS 将环境信息和合成用户输入融合到 Deep Q Learning Network（DQN）流程中，形成交互式端到端自训练机制，从而为基于末端执行器的机器人控制生成平滑轨迹。所提出框架在没有任何预先存在数据集的情况下，在动态目标估计与获取的仿真中取得了 98% 的高性能纪录。作为一项零样本 sim-to-real 用户研究，五名参与者使用嘈杂的头部运动控制物理机械臂；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表现出显著改善，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为 44%。STREAMS 旨在通过提供一种交互式、端到端的解决方案来稳定末端执行器轨迹，从而改进基于生物信号的辅助机器人控制，提升任务性能和准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
