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.
Early handheld robot work @cite_4 used a trunk-shaped robot with 4-DoF to explore issues of autonomy and task performance. This was later upgraded to a 6-DoF (joint space) mechanism @cite_14 and used gestures, such as pointing, to study user guidance. These earlier works demonstrate how users benefit from the robot's quick and accurate movement while the robot profits from the human's tactical motion. Most importantly, increased cooperative performance was measured with an increased level of the robot's autonomy. They furthermore found that cooperative performance significantly increases when the robot communicates its plans e.g. via a robot-mounted display @cite_7 . Within this series of work, another problem was identified: the robot does not sense the user's intention and thus potential conflicts with the robot's plan remain unsolved. For example, when the user would point the robot towards a valid subsequent goal, the robot might have already chosen a different one and keep pointing towards it rather than adapting its task plan. This led to irritation and frustration in users on whom the robot's plan was imposed on.
@cite_14 @cite_4 @cite_7
2537120235 1963832262 1992461594 1514550370
Human-scale mobile robots with arms have the potential to assist people with a variety of tasks. We present a proof-of-concept system that has enabled a person with severe quadriplegia named Henry Evans to shave himself in his own home using a general purpose mobile manipulator (PR2 from Willow Garage). The robot primarily provides assistance by holding a tool (e.g., an electric shaver) at user-specified locations around the user's head, while he she moves his her head against it. If the robot detects forces inappropriate for the task (e.g., shaving), it withdraws the tool. The robot also holds a mirror with its other arm, so that the user can see what he she is doing. For all aspects of the task, the robot and the human work together. The robot uses a series of distinct semi-autonomous subsystems during the task to navigate to poses next to the wheelchair, attain initial arm configurations, register a 3D model of the person's head, move the tool to coarse semantically-labeled tool poses (e.g, “Cheek”), and finely position the tool via incremental movements. Notably, while moving the tool near the user's head, the robot uses an ellipsoidal coordinate system attached to the 3D head model. In addition to describing the complete robotic system, we report results from Henry Evans using it to shave both sides of his face while sitting in his wheelchair at home. He found the process to be long (54 minutes) and the interface unintuitive. Yet, he also found the system to be comfortable to use, felt safe while using it, was satisfied with it, and preferred it to a human caregiver. We describe a learning strategy that allows a humanoid robot to autonomously build a representation of its workspace: we call this representation Reachable Space Map. Interestingly, the robot can use this map to: (i) estimate the Reachability of a visually detected object (i.e. judge whether the object can be reached for, and how well, according to some performance metric) and (ii) modify its body posture or its position with respect to the object to achieve better reaching. The robot learns this map incrementally during the execution of goal-directed reaching movements; reaching control employs kinematic models that are updated online as well. Our solution is innovative with respect to previous works in three aspects: the robot workspace is described using a gaze-centered motor representation, the map is built incrementally during the execution of goal-directed actions, learning is autonomous and online. We implement our strategy on the 48-DOFs humanoid robot Kobian and we show how the Reachable Space Map can support intelligent reaching behavior with the whole-body (i.e. head, eyes, arm, waist, legs). In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human's motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by computing the swept volume of learned human motion trajectories. The motion planner then plans robot trajectories that minimize a penetration cost in the human workspace occupancy while interleaving planning and execution. Multiple plans are computed in parallel, one for each robot task available at the current time, and the trajectory with the least cost is selected for execution. We test our framework in simulation using recorded human motions and a simulated PR2 robot. Our results show that our framework enables the robot to avoid the human while still accomplishing the robot's task, even in cases where the initial prediction of the human's motion is incorrect. We also show that taking into account the predicted human workspace occupancy in the robot's motion planner leads to safer and more efficient interactions between the user and the robot than only considering the human's current configuration. 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.