Dexterous robot hand teleoperation allows for long-range transfer of human manipulation expertise, and could simultaneously provide a way for humans to teach these skills to robots. However, current methods struggle to reproduce the functional workspace of the human hand, often limiting them to simple grasping tasks. We present a novel method for finger-gaited manipulation with multi-fingered robot hands. Our method provides the operator enhanced flexibility in making contacts by expanding the reachable workspace of the robot hand through residual Gaussian Process learning. We also assist the operator in maintaining stable contacts with the object by allowing them to constrain fingertips of the hand to move in concert. Extensive quantitative evaluations show that our method significantly increases the reachable workspace of the robot hand and enables the completion of novel dexterous finger gaiting tasks. Project website: http://respilot-hri.github.io

In this work, we introduce PokeRRT, a novel motion planning algorithm that demonstrates poking as an effective non-prehensile manipulation skill to enable fast manipulation of objects and increase the size of a robot's reachable workspace. We showcase poking as a failure recovery tactic used synergistically with pick-and-place for resiliency in cases where pick-and-place initially fails or is unachievable. Our experiments demonstrate the efficiency of the proposed framework in planning object trajectories using poking manipulation in uncluttered and cluttered environments. In addition to quantitatively and qualitatively demonstrating the adaptability of PokeRRT to different scenarios in both simulation and real-world settings, our results show the advantages of poking over pushing and grasping in terms of success rate and task time.

Steerable needles are minimally invasive devices that enable novel medical procedures by following curved paths to avoid critical anatomical obstacles. Planning algorithms can be used to find a steerable needle motion plan to a target. Deployment typically consists of a physician manually inserting the steerable needle into tissue at the motion plan's start pose and handing off control to a robot, which then autonomously steers it to the target along the plan. The handoff between human and robot is critical for procedure success, as even small deviations from the start pose change the steerable needle's workspace and there is no guarantee that the target will still be reachable. We introduce a metric that evaluates the robustness to such start pose deviations. When measuring this robustness to deviations, we consider the tradeoff between being robust to changes in position versus changes in orientation. We evaluate our metric through simulation in an abstract, a liver, and a lung planning scenario. Our evaluation shows that our metric can be combined with different motion planners and that it efficiently determines large, safe start regions.

A novel kinematically redundant (6+3)-DoF parallel robot is presented in this paper. Three identical 3-DoF RU/2-RUS legs are attached to a configurable platform through spherical joints. With the selected leg mechanism, the motors are mounted at the base, reducing the reflected inertia. The robot is intended to be actuated with direct-drive motors in order to perform intuitive physical human-robot interaction. The design of the leg mechanism maximizes the workspace in which the end-effector of the leg can have a 2g acceleration in all directions. All singularities of the leg mechanism are identified under a simplifying assumption. A CAD model of the (6+3)-DoF robot is presented in order to illustrate the preliminary design of the robot.

Aligning humans' assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a joint task. In this work, we propose an approach to calibrate humans' estimate of a robot's reachable workspace through a small number of demonstrations before collaboration. We develop a novel motion planning method, REMP (Reachability-Expressive Motion Planning), which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot's motion capability to a human observer. Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations in a subsequent joint task.

Cable-driven parallel robots (CDPRs) are widely investigated and applied in the worldwide; however, traditional configurations make them to be limited in reaching their maximum workspace duo to constraints such as the maximum allowable tensions of cables. In this paper, we introduce auxiliary counterbalances to tackle this problem and focus on workspace analysis and optimal design of CDPRs with such systems. Besides, kinematics, dynamics, and parameters optimization formulas and algorithm are provided to maximize the reachable workspace of CDPRs. Case studies for different configurations are presented and discussed. Numerical results suggest the effectiveness of the aforementioned approaches, and the obtained parameters can also be applied for actual CDPRs design.