Robotic manipulation in cluttered environments requires synergistic planning among prehensile and non-prehensile actions. Previous works on sampling-based Task and Motion Planning (TAMP) algorithms, e.g. PDDLStream, provide a fast and generalizable solution for multi-modal manipulation. However, they are likely to fail in cluttered scenarios where no collision-free grasping approaches can be sampled without preliminary manipulations. To extend the ability of sampling-based algorithms, we integrate a vision-based Reinforcement Learning (RL) non-prehensile procedure, pusher . The pushing actions generated by pusher can eliminate interlocked situations and make the grasping problem solvable. Also, the sampling-based algorithm evaluates the pushing actions by providing rewards in the training process, thus the pusher can learn to avoid situations leading to irreversible failures. The proposed hybrid planning method is validated on a cluttered bin-picking problem and implemented in both simulation and real world. Results show that the pusher can effectively improve the success ratio of the previous sampling-based algorithm, while the sampling-based algorithm can help the pusher learn pushing skills.

This paper presents a study on the effect of design parameters of an underactuated hand on its grasp performance. Three kinds of grasp performance characteristics are considered: grasp range, grasp strength, and immobilizing grasp range. Grasp strength is defined as the stiffness of the grasp. Immobilizing grasps are those in which the object cannot be moved for a force up to a certain threshold. In general, underactuated hands cannot produce immobilizing grasps. However, we show that immobilizing grasps can be created by including joint limits in the hand design. We consider the effect of two design parameters on the grasp performance: torque ratio and finger-base distance. Results show that an optimal finger-base distance and torque ratio exists that maximizes the grasp range and grasp strength. Also, the immobilizing grasp range is increased by decreasing the finger-base distance and increasing the torque ratio and joint limits.

As the industry shifts to automated manufacturing and the assembly of parts in smaller batches, there is a clear need for an efficient design of grippers. This paper presents a method for automated grasp planning and finger design for multiple parts using four grasp quality measures that capture the following important requirements for grasping: (i) uniform contact force distribution; (ii) better gravity wrench resistance; (iii) robustness against gripper positioning error; and (iv) ability to resist larger external wrench on the object. We introduce the fingertip score to quantify the grasp performance of a fingertip design over all the objects. The method takes the CAD model of the objects as the input and outputs the optimal grasp location and the best finger design. We use the method for a three-point grasp with a parallel jaw gripper. We validate our method on two sets of objects. Results show how each grasp quality measure behaves on different objects and the variation in the fingertip score with finger design. Finally, we test the effectiveness of the optimal finger design experimentally. The three-point grasp is suitable for grasping objects larger than is possible with shape-matching fingertips.

Continuum robot-based surgical systems are becoming an effective tool for minimally invasive surgery. A flexible, dexterous, and compact robot structure is suitable for carrying out complex surgical operations. In this paper, we propose performance metrics for dexterity based on data density. Data density at a point in the workspace is higher if the number of reachable points is higher, with a unique configuration lying in a small square box around a point. The computation of these metrics is performed with forward kinematics using the Monte Carlo method and, hence, is computationally efficient. The data density at a particular point is a measure of dexterity at that point. In contrast, the dexterity distribution property index is a measure of how well dexterity is distributed across the workspace according to desired criteria. We compare the dexterity distribution property index across the workspace with the dexterity index based on the dexterous solid angle and manipulability-based approach. A comparative study reveals that the proposed method is simple and straightforward because it uses only the position of the reachable point as the input parameter. The method can quantify and compare the performance of different geometric designs of hyper-redundant and multisegment continuum robots based on dexterity.