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This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints...
Grasp quality metrics which analyze the contact wrench space are commonly used to synthesize and analyze preplanned grasps. Preplanned grasping approaches rely on the robustness of stored solutions. Analyzing the robustness of such solutions for large databases of preplanned grasps is a limiting factor for the applicability of data driven approaches to grasping. In this work, we will focus on the...
This paper presents an approach for computing power grasps for hands with kinematic structure similar to the human hand, which allows the implementation of strategies inspired in human grasping actions. The proposed method first samples the object surface to look for the best spots for creating an opposing grasp with two or three fingers, and then aligns the other fingers to match the local curvature...
This paper proposes a method to construct Navigation Functions (NF) from experimental trajectories in an unknown environment. We want to approximate an unknown obstacle function and then use it within an NF. When navigating the same destinations with the experiments, this NF should produce the same trajectories as the experiments. This requirement is equivalent to a partial differential equation (PDE)...
This paper explores how Cloud Computing can facilitate grasping with shape uncertainty. We consider the most common robot gripper: a pair of thin parallel jaws, and a class of objects that can be modeled as extruded polygons. We model a conservative class of push-grasps that can enhance object alignment. The grasp planning algorithm takes as input an approximate object outline and Gaussian uncertainty...
Many manipulation planning problems involve several related sub-problems, such as the selection of grasping points on an object, choice of hand posture, and determination of the arm's configuration and evolving trajectory. Traditionally, these planning sub-problems have been handled separately, potentially leading to sub-optimal, or even infeasible, combinations of the individually determined solutions...
Compliance in multifingered hand improves grasp stability and effectiveness of the manipulation tasks. Compliance of robotic hands depends mainly on the joint control parameters, on the mechanical design of the hand, as joint passive springs, and on the contact properties. In object grasping the primary task of the robotic hand is the control of internal forces which allows to satisfy the contact...
This paper presents a novel force learning framework to learn fingertip force for a grasping and manipulation process from a human teacher with a force imaging approach. A demonstration station is designed to measure fingertip force without attaching force sensor on fingertips or objects so that this approach can be used with daily living objects. A Gaussian Mixture Model (GMM) based machine learning...
Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points,...
This paper proposes a new strategy for making a knot with a general-purpose arm. The strategy is divided into four stages: First, a human-performed knotting action is analyzed to acquire some motion primitives. Second, based on these databases, the robot is taught to tie a knot. Third, key points on the stored trajectories are extracted. These key points are considered in a general case, and then...
In this paper, a method to derive the three predominant synergies and their temporal weights for planning grasps of the UB Hand IV (University of Bologna Hand, version IV) is proposed. The method adopted to define the postural synergies from experiments is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. The control strategy, exploiting postural...
This paper proposes a method for planning the pick-and-place motion of an object by dual-arm manipulators. Our planner is composed of the offline and the online phases. The offline phase generates a set of regions on the object and the environment surfaces and calculates several parameters needed in the online phase. In the online phase, the planner selects a grasping pose of the robot and a putting...
Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the...
The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects,...
Grasping has been studied from various perspectives including planning, control, and learning. In this paper, we take a learning approach to predict successful grasps for a universal jamming gripper. A jamming gripper is comprised of a flexible membrane filled with granular material, and it can quickly harden or soften to grip objects of varying shape by modulating the air pressure within the membrane...
We deal with the problem of blind grasping where we use tactile feedback to predict the stability of a robotic grasp given no visual or geometric information about the object being grasped. We first simulated tactile feedback using a soft finger contact model in GraspIt! [1] and computed tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database [2]. We used the...
Many one-degree-of-freedom (1-DOF) grippers have been used in factories. This paper focuses on the design of the 1-DOF parts handling device for picking up small objects robustly and agilely and realizing assembly tasks. In our conventional research, we proposed a concept for the handling device, which cages an object without letting the object escape from its tips before closing them completely and...
In order to implement promising robot applications in our daily lives, robots need to perform manipulation tasks within the human environments. Especially for a humanoid robot, it is essential to manipulate a variety of objects with different shapes and sizes to assist humans in the human environments. This paper presents a method of manipulating objects with humanoid robot's dual arms. The robot...
The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot...
The demands of mobile manipulation are leading to a new class of multi-fingered hands with a premium on being lightweight and robust as well as being able to grasp and perform basic manipulations with a wide range of objects. A promising approach to addressing these goals is to use compliant, underactuated hands with selectively lockable degrees of freedom. This paper presents the design of one such...
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