NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit, Università Campus Bio-Medico di Roma, Rome, Italy.
Neurorobotics Group, Newcastle University, Newcastle, United Kingdom.
PLoS One. 2023 Jun 8;18(6):e0286566. doi: 10.1371/journal.pone.0286566. eCollection 2023.
Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would also be helpful when a user aims at controlling a robotic arm proficiently. However, whether the position of the robot and its continuous update should be coded in a extrinsic or intrinsic reference frame is not known. Here we compared two different supplementary feedback contents concerning the status of a robotic limb in 2-DoFs configuration: one encoding the Cartesian coordinates of the end-effector of the robotic arm (i.e., Task-space feedback) and another and encoding the robot joints angles (i.e., Joint-space feedback). Feedback was delivered to blindfolded participants through vibrotactile stimulation applied on participants' leg. After a 1.5-hour training with both feedbacks, participants were significantly more accurate with Task compared to Joint-space feedback, as shown by lower position and aiming errors, albeit not faster (i.e., similar onset delay). However, learning index during training was significantly higher in Joint space feedback compared to Task-space feedback. These results suggest that Task-space feedback is probably more intuitive and more suited for activities which require short training sessions, while Joint space feedback showed potential for long-term improvement. We speculate that the latter, despite performing worse in the present work, might be ultimately more suited for applications requiring long training, such as the control of supernumerary robotic limbs for surgical robotics, heavy industrial manufacturing, or more generally, in the context of human movement augmentation.
本体感觉极大地提高了我们控制自身身体的能力。这表明，当用户想要熟练地控制机器臂时，补充触觉感官反馈也会有所帮助。然而，机器人的位置及其连续更新应该以外在还是内在参考系进行编码尚不清楚。在这里，我们比较了两种不同的关于机器人肢体在 2-DoFs 配置中的状态的补充反馈内容：一种编码机器人手臂末端执行器的笛卡尔坐标（即任务空间反馈），另一种编码机器人关节角度（即关节空间反馈）。反馈通过应用于参与者腿部的振动触觉刺激提供给蒙住眼睛的参与者。在两种反馈的 1.5 小时培训后，与关节空间反馈相比，参与者在任务空间反馈中明显更准确，表现为位置和瞄准误差较低，尽管速度没有更快（即，起始延迟相似）。然而，在训练过程中的学习指数在关节空间反馈中明显高于任务空间反馈。这些结果表明，任务空间反馈可能更直观，更适合需要短期培训的活动，而关节空间反馈则显示出长期改进的潜力。我们推测，后者尽管在本工作中表现不佳，但可能最终更适合需要长时间培训的应用，例如用于手术机器人、重工业制造或更一般地在人类运动增强背景下的多余机器臂控制。
