Many robotic applications, such as sanding, polishing, wiping and sensor scanning, require a manipulator to dexterously cover a surface using its end-effector. In this paper, we provide an efficient and effective coverage path planning approach that leverages a manipulator's redundancy and task tolerances to minimize costs in joint space. We formulate the problem as a Generalized Traveling Salesman Problem and hierarchically streamline the graph size. Our strategy is to identify guide paths that roughly cover the surface and accelerate the computation by solving a sequence of smaller problems. We demonstrate the effectiveness of our method through a simulation experiment and an illustrative demonstration using a physical robot.

Many applications require a robot to accurately track reference end-effector trajectories. Certain trajectories may not be tracked as single, continuous paths due to the robot’s kinematic constraints or obstacles elsewhere in the environment. In this situation, it becomes necessary to divide the trajectory into shorter segments. Each such division introduces a reconfiguration, in which the robot deviates from the reference trajectory, repositions itself in configuration space, and then resumes task execution. The occurrence of reconfigurations should be minimized because they increase time and energy usage. In this paper, we present IKLink, a method for finding joint motions to track reference end-effector trajectories while executing the minimum number of reconfigurations. Our graph-based method generates a diverse set of Inverse Kinematics (IK) solutions for every waypoint on the reference trajectory and utilizes a dynamic programming algorithm to find the optimal motion by linking the IK solutions. We demonstrate the effectiveness of IKLink through a simulation experiment and an illustrative demonstration using a physical robot.

Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks with a range of goals. In this paper, we propose a real-time motion generation method that accommodates all three categories of tasks within a single, unified framework and leverages the flexibility of tasks with a range of goals to accommodate other tasks. Our method incorporates tasks in a weighted-sum multiple-objective optimization structure and uses barrier methods with novel loss functions to encode the valid range of a task. We demonstrate the effectiveness of our method through a simulation experiment that compares it to state-of-the-art alternative approaches, and by demonstrating it on a physical camera-in-hand robot that shows that our method enables the robot to achieve smooth and feasible camera motions.

We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the user's control less direct. In this work, we implemented a telema-nipulation system that allows a robot to autonomously adjust its configuration within task tolerances. We conducted a user study comparing a telemanipulation paradigm that exploits task tolerances (functional mimicry) to a paradigm that requires the robot to exactly mimic its human operator (exact mimicry), and assess how the choice in paradigm shapes user experience and task performance. Our results show that autonomous adjustments within task tolerances can lead to performance improvements without sacrificing perceived control of the robot. Additionally, we find that users perceive the robot to be more under control, predictable, fluent, and trustworthy in functional mimicry than in exact mimicry.

We investigate how robotic camera systems can offer new capabilities to computer-supported cooperative work through the design, development, and evaluation of a prototype system called Periscope. With Periscope, a local worker completes manipulation tasks with guidance from a remote helper who observes the workspace through a camera mounted on a semi-autonomous robotic arm that is co-located with the worker. Our key insight is that the helper, the worker, and the robot should all share responsibility of the camera view-an approach we call shared camera control. Using this approach, we present a set of modes that distribute the control of the camera between the human collaborators and the autonomous robot depending on task needs. We demonstrate the system's utility and the promise of shared camera control through a preliminary study where 12 dyads collaboratively worked on assembly tasks. Finally, we discuss design and research implications of our work for future robotic camera systems that facilitate remote collaboration.

In telemanipulation, showing the user multiple views of the remote environment can offer many benefits, although such different views can also create a problem for control. Systems must either choose a single fixed control frame, aligned with at most one of the views or switch between view-aligned control frames, enabling view-aligned control at the expense of switching costs. In this paper, we explore the trade-off between these options. We study the feasibility, benefits, and drawbacks of switching the user's control frame to align with the actively used view during telemanipulation. We additionally explore the effectiveness of explicit and implicit methods for switching control frames. Our results show that switching between multiple view-specific control frames offers significant performance gains compared to a fixed control frame. We also find personal preferences for explicit or implicit switching based on how participants planned their movements. Our findings offer concrete design guidelines for future multi-camera interfaces.

We explore first-person demonstration as an intuitive way of producing task demonstrations to facilitate user-centric robotic assistance. First-person demonstration directly captures the human experience of task performance via head-mounted cameras and naturally includes productive viewpoints for task actions. We implemented a perception system that parses natural first-person demonstrations into task models consisting of sequential task procedures, spatial configurations, and unique task viewpoints. We also developed a robotic system capable of interacting autonomously with users as it follows previously acquired task demonstrations. To evaluate the effectiveness of our robotic assistance, we conducted a user study contextualized in an assembly scenario; we sought to determine how assistance based on a first-person demonstration (user-centric assistance) versus that informed only by the cover image of the official assembly instruction (standard assistance) may shape users' behaviors and overall experience when working alongside a collaborative robot. Our results show that participants felt that their robot partner was more collaborative and considerate when it provided user-centric assistance than when it offered only standard assistance. Additionally, participants were more likely to exhibit unproductive behaviors, such as using their non-dominant hand, when performing the assembly task without user-centric assistance.