Within this work, we explore intention inference for user actions in the context of a handheld robot setup. Handheld robots share the shape and properties of handheld tools while being able to process task information and aid manipulation. Here, we propose an intention prediction model to enhance cooperative task solving. Within a block copy task, we collect eye gaze data using a robot-mounted remote eye tracker which is used to create a profile of visual attention for task-relevant objects in the workspace scene. These profiles are used to make predictions about user actions i.e. which block will be picked up next and where it will be placed. Our results show that our proposed model can predict user actions well in advance with an accuracy of 87.94 (500ms prior) for picking and 93.25 (1500 ms prior) for placing actions.
We present a novel 6-DoF cable driven manipulator for handheld robotic tasks. Based on a coupled tendon approach, the arm is optimized to maximize movement speed and configuration space while reducing the total mass of the arm. We propose a space carving approach to design optimal link geometry maximizing structural strength and joint limits while minimizing link mass. The design improves on similar non-handheld tendon-driven manipulators and reduces the required number of actuators to one per DoF. As the manipulator has one redundant joint, we present a 5-DoF inverse kinematics solution for the end effector pose. The inverse kinematics is solved by splitting the 6-DoF problem into two coupled 3-DoF problems and merging their results. A method for gracefully degrading the output of the inverse kinematics is described for cases where the desired end effector pose is outside the configuration space. This is useful for settings where the user is in the control loop and can help the robot to get closer to the desired location. The design of the handheld robot is offered as open source. While our results and tools are aimed at handheld robotics, the design and approach is useful to non-handheld applications. This paper concerns itself with a relatively unexplored type of personal robot that operates in the tool space. Handheld robots aim to cooperate with the user to solve tasks and improve what tools can offer enhanced by actuation, sensing, and importantly, task knowledge. To this end, we devised a new lightweight robotic platform that has 4 DoF and uses a cable driven continuum structure. Feedback from the robot to the user is provided in an intuitive, implicit manner by the robot end effector pointing towards the goal, avoiding pointing, and or refusing to perform an action when it conflicts with the task specification. We evaluate two generic tasks involving aiming in space and picking placing objects with a number of volunteers. Repeated measures ANOVA is used to analyse results to show in which conditions an increased level of automation in the handheld robot improves task performance or user perception of task load. The robot is offered as an open robotics platform[1] and the results indicate directions to improve on feedback and interaction mechanisms. In this paper we address the question of how to provide feedback information to guide users of a handheld robotic device when performing a spa- tial exploration task. We consider various feedback methods for communicating a five degree of freedom target pose to a user including a stereoscopic VR display, a monocular see-through AR display and a 2D screen as well as robot arm gesturing. The spatial exploration task with the handheld robot arm was compared against a baseline of a passive handheld wand. We compared the performance of each of the methods with 21 volunteers using a repeated measures ANOVA experimental design, and recorded users' opinions via a NASA Task Load Index Survey. The robot assisted reaching feedback methods significantly outperform manual reaching with the wand for all three visual feedback methods. However, there is little difference between each of the three visual feedback methods when using the robot. The completion time of the task varies with changing difficulty when using the wand but remains stable when assisted by the robot. These results convey useful information for the design of cooperative handheld robots.