Fine manipulation is important in dexterous tasks executed via teleoperation, including in robot-assisted surgery. Discovering fundamental laws of human movement can benefit the design and control of teleoperated systems, and the training of their users. These laws are formulated as motor invariants, such as the well-studied speed-curvature power law. However, while the majority of these laws characterize translational movements, fine manipulation requires controlling the orientation of objects as well. This subject has received little attention in human motor control studies. Here, we report a new power law linking the speed to the geometry in orientation control -humans rotate their hands with an angular speed that is exponentially related to the local change in the direction of rotation. We demonstrate this law in teleoperated tasks performed by surgeons using surgical robotics research platforms. Additionally, we show that the law's parameters change slowly with the surgeons' training, and are robust within participants across task segments and repetitions. The fact that this power law is a robust motor invariant suggests that it may be an outcome of sensorimotor control. It also opens questions about the nature of this control and how it can be harnessed for better control of human-teleoperated robotic systems.

Computational approaches to biological motor control are used to discover the building blocks of human motor behavior. Models explaining features of human hand movements have been studied thoroughly, yet only a few studies attempted to explain the control of the orientation of the hand; instead, they mainly focus on the control of hand translation, predominantly in a single plane. In this study, we aimed to establish a basic understanding of the way humans control the orientation of their hands. We developed a quaternion-based score that quantifies the geodicity of rotational hand movements and evaluated it experimentally. In the first experiment participants performed a simple orientation-matching task with a robotic manipulator. We found that rotations are generally performed by following a geodesic in the quaternion hypersphere, which suggests that, similarly to translation, the orientation of the hand is centrally controlled. We also established a baseline for the study of human response to perturbed visual feedback of the orientation of the hand. In the subsequent second experiment we studied the adaptation of participants to visuomotor rotation that is applied on the hand's rotation, and the transfer of the adaptation to a different initial orientation. We observed partial adaptation to the perturbation. The patterns of the transfer of the adaptation to a different initial orientation were consistent with the representation of the orientation in extrinsic coordinates. The results of the two experiments raise questions regarding the nature of central control of hand orientation. Discussion and intuitions from these results can be of benefit for many applications that involve fine manipulation of rigid bodies, such as teleoperation and neurorehabilitation.

Computational approaches to biological motor control are used to discover the building blocks of human motor behaviour. Models explaining features of human hand movements have been studied thoroughly, yet only a few studies attempted to explain the control of the orientation of the hand; instead, they mainly focus on the control of hand translation, predominantly in a single plane. In this study, we present a new methodology to study the way humans control the orientation of their hands in three dimensions and demonstrate it in two sequential experiments. We developed a quaternion-based score that quantifies the geodicity of rotational hand movements and evaluated it experimentally. In the first experiment, participants performed a simple orientation-matching task with a robotic manipulator. We found that rotations are generally performed by following a geodesic in the quaternion hypersphere, which suggests that, similarly to translation, the orientation of the hand is centrally controlled, possibly by optimizing geometrical properties of the hand’s rotation. This result established a baseline for the study of human response to perturbed visual feedback of the orientation of the hand. In the second experiment, we developed a novel visuomotor rotation task in which the rotation is applied on the hand’s rotation, and studied the adaptation of participants to this rotation, and the transfer of the adaptation to a different initial orientation. We observed partial adaptation to the rotation. The patterns of the transfer of the adaptation to a different initial orientation were consistent with the representation of the orientation in extrinsic coordinates. The methodology that we developed allows for studying the control of a rigid body without reducing the dimensionality of the task. The results of the two experiments open questions for future studies regarding the mechanisms underlying the central control of hand orientation. These results can be of benefit for many applications that involve fine manipulation of rigid bodies, such as teleoperation and neurorehabilitation.

Fine manipulation is important in dexterous tasks executed via teleoperation, including in robot-assisted surgery. Discovering fundamental laws of human movement can benefit the design and control of teleoperated systems, and the training of their users. These laws are formulated as motor invariants, such as the well-studied speed-curvature power law. However, while the majority of these laws characterise translational movements, fine manipulation requires controlling the orientation of objects. This subject has received little attention in human motor control studies. Here, we report a new power law linking the speed to the geometry in orientation control – humans rotate their hands with an angular speed that is exponentially related to the local change in rotation direction. We demonstrate this law in a teleoperated task performed by surgeons with a surgical robotics research platform. Additionally, we show that the law’s parameters change slowly with the surgeons’ training, and are robust within participants across task segments and repetitions. The fact that this power law is a robust motor invariant suggests that it may be an outcome of sensorimotor control. It also opens questions about the nature of this control and how it can be harnessed for better control of human-teleoperated robotic systems.