J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators

J-PARSE is a method for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities. The commanded end-effector velocity is interpreted component-wise, according to the available mobility in each dimension of the task space. First, a substitute "Safety" Jacobian matrix is created, keeping the aspect ratio of the manipulability ellipsoid above a threshold value. The desired motion is then projected onto non-singular and singular directions, and the latter projection scaled down by a factor informed by the threshold value. A right-inverse of the non-singular Safety Jacobian is applied to the modified command. In the absence of joint limits and collisions, this ensures smooth transition into and out of low-rank poses, guaranteeing asymptotic stability for target poses within the workspace, and stability for those outside. Velocity control with J-PARSE is benchmarked against the Least-Squares and Damped Least-Squares inversions of the Jacobian, and shows high accuracy in reaching and leaving singular target poses. By expanding the available workspace of manipulators, the method finds applications in servoing, teleoperation, and learning.

ProACT: An Augmented Reality Testbed for Intelligent Prosthetic Arms

Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. Using ProACT with non-amputee participants, we compare performance in a Box-and-Blocks Task using a virtual myoelectric prosthetic arm, with and without intent estimation. Our results show that methods using intent estimation improve both user satisfaction and the degree of success in the task. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics.

Considerations for the Control Design of Augmentative Robots

Robotic systems that are intended to augment human capabilities commonly require the use of semi-autonomous control and artificial sensing, while at the same time aiming to empower the user to make decisions and take actions. This work identifies principles and techniques from the literature that can help to resolve this apparent contradiction. It is postulated that augmentative robots must function as tools that have partial agency, as collaborative agents that provide conditional transparency, and ideally, serve as extensions of the human body.