This article presents an interactive control approach allowing a human user to teleoperate a robotic manipulator located nearby. With this approach, the user keeps his/her hands free, as only head movements are exploited to control the robot. The controller maps the 6 Degrees of Freedom (DoF) user's head position and orientation into the 6 DoF robot endeffector position and orientation. The robot can reach a large workspace thanks to the combination of two features. Firstly, a virtual wand between the user's head and the robot end-effector converts user's head pantilt rotations into large displacements of the robot end-effector center perpendicularly to the wand axis (2 DoF). Secondly, for the remaining 4 DoF (robot end-effector center displacement along the wand axis and robot en-effector orientation), realtime deformation of the virtual wand is triggered when the user reaches uncomfortable configurations due to his/her head workspace limitations. Additionally, the user gets, through an Augmented Reality (AR) Headset, a non-delayed visual feedback of the current virtual wand geometry and location. The paper includes a description of the setup and the proposed controller, detailing how the robot position/orientation is coupled to the user's head position/orientation. A set of elementary experiments with a constant-geometry wand is first presented, showing workspace limitations for some DoF. Then the wand reconfiguration is introduced in the experiments, leading to full control of 6 DoF manipulation tasks throughout a large workspace.

The prevailing and most effective approach to teleoperate a robotic arm involves a direct position-to-position mapping, imposing robotic end-effector movements that mirrors those of the user, Fig. 1-top. However, due to this one-to-one mapping, the robot's motions are limited by the user's capability, particularly in translation. Drawing inspiration from head pointers utilized in the 1980s, originally designed to enable drawing with limited head motions for tetraplegic individuals, we proposed a “virtual wand” mapping which could be used by participants with reduced mobility. This mapping employs a virtual rigid linkage between the hand and the robot's endeffector, Fig. 1-bottom. With this approach, rotations produce amplified translations through a lever arm, creating a “rotation-to-position” coupling and expanding the translation workspace at the expense of a reduced rotation space. In this study, we compare the virtual wand approach to the one-to-one position mapping through the realization of 6-DoF reaching tasks. Results indicate that the two different mappings perform comparably well, are equally well-received by users, and exhibit similar motor control behaviors. Nevertheless, the virtual wand mapping is anticipated to outperform in tasks characterized by large translations and minimal effector rotations, whereas direct mapping is expected to demonstrate advantages in large rotations with minimal translations. These results pave the way for new interactions and interfaces, particularly in disability assistance utilizing residual body movements (instead of hands) as control input. Leveraging body parts with substantial rotations could enable the accomplishment of tasks previously deemed infeasible with standard direct coupling interfaces.

When the arm prosthesis worn by an amputated Human being is not adequately configured with respect to the end-effector task, body compensations are often observed. Namely, to compensate for a wrong joint positioning on the robotic distal side, a subject trying to reach a desired position/orientation of his/her hand mobilizes his/her proximal joints, thus exploiting the redundancy of the human+robot kinematic chain.In this paper, we explore the possibility of exploiting this well-known behavior to reverse the causality: if we observe the posture of an amputated subject wearing a prosthesis during a hand positioning task, to what extent can we infer the positioning error of the prosthesis?To answer this question, we make the assumption that the adequate, or natural posture for a given task is one that optimizes a postural score. The proposed approach then consists in i) measuring the joint posture of the subject fitted with the prosthesis; ii) search for an alternative posture that optimizes a postural score within the null space of the human+robot kinematic chain and iii) compute the position error for the robot joints between the initial and the optimized posture.An experimental evaluation is provided with non amputated subjects who emulate erratic positioning of their distal joints during hand positioning tasks. Results show that joint errors are estimated with a precision that seems compatible with the implementation of a real time control algorithm.