Handling Objects by Their Handles

This paper presents an efficient method to decide robust grasps given new objects using example-based learning. A robust grasp is a stable grasp, suitable for object manipulation. Adaptability to object manipulation is ensured by imitating the human choice of the object grasping component, its handle. Stability is obtained by computing contact points, ensuring force-closure property, on that handle

On Computing Robust N-Finger Force-Closure Grasps of 3D Objects

The paper deals with computing frictional forceclosure grasps of 3D objects problem. The key idea of the presented work is the demonstration that wrenches associated to any three non-aligned contact points of 3D objects form a basis of their corresponding wrench space. This result permits the formulation of a new sufficient force-closure test. Our approach works with general objects, modelled with a set of points, and with any number n of contacts (n >= 4). A quality criterion is also introduced. A corresponding algorithm for computing robust force-closure grasps has been developed. Its efficiency is confirmed by comparing it to the classical convexhull method

A Sufficient Condition for Force-Closure Grasps Synthesis of 3D Objects

A Sufficient Condition for Force-Closure Grasps Synthesis of 3D Object

Learning the natural grasping component of an unknown object

A grasp is the beginning of any manipulation task. Therefore, an autonomous robot should be able to grasp objects it sees for the first time. It must hold objects appropriately in order to successfully perform the task. This paper considers the problem of grasping unknown objects in the same manner as humans. Based on the idea that the human brain represents objects as volumetric primitives in order to recognize them, the presented algorithm predicts grasp as a function of the object’s parts assembly. Beginning with a complete 3D model of the object, a segmentation step decomposes it into single parts. Each single part is fitted with a simple geometric model. A learning step is finally needed in order to find the object component that humans choose to grasp it

Apprentissage de la partie préhensible d'un objet de forme quelconque

Apprentissage de la partie préhensible d'un objet de forme quelconqu

Changing human upper-limb synergies with an exoskeleton using viscous fields

International audienceRobotic exoskeletons can apply forces distributed on the limbs of the subject they are connected to. This offers a great potential in the field of neurorehabilitation, to address the impairment of interjoint coordination in hemiparetic stroke patients. In these patients, the normal flexible joint rotation synergies are replaced by pathological fixed patterns of rotation. In this paper, we investigate how the concept of synergy can be exploited in the control of an upper limb exoskeleton. The long term goal is to develop a device capable of changing the joint synchronization of a patient performing exercises during rehabilitation. The paper presents a controller able of generating joint viscous torques in such a way that constraints on joint velocities can be imposed to the subject without constraining the hand motion. On another hand, the same formalism is used to describe synergies observed on the arm joint motion of subjects realizing pointing tasks. This approach is experimented on a 4 Degrees Of Freedom (DoF) upper arm exoskeleton with subjects performing pointing 3-dimensional tasks. Results exhibit the basic properties of the controller and show its capacity to impose an arbitrary chosen synergy without affecting the hand motion

Dexterous manipulation planning using probabilistic roadmaps in continuous grasp subspaces

In this paper, we propose a new method for the motion planning problem of rigid object dexterous manipulation with a robotic multi-fingered hand, under quasi-static movement assumption. This method computes both object and finger trajectories as well as the finger relocation sequence. Its specificity is to use a special structuring of the research space that allows to search for paths directly in the particular subspace GSn which is the subspace of all the grasps that can be achieved with n grasping fingers. The solving of the dexterous manipulation planning problem is based upon the exploration of this subspace. The proposed approach captures the connectivity of GSn in a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method

Fast Grasp Planning Using Cord Geometry

International audienceIn this paper, we propose a novel idea to address theproblem of fast computation of stable force-closure grasp configurationsfor a multifingered hand and a 3-D rigid object representedas a polygonal soup model. The proposed method performsa low-level shape exploration by wrapping multiple cords aroundthe object in order to quickly isolate promising grasping regions.Around these regions, we compute grasp configurations by applyinga variant of the close-until-contact procedure to find thecontact points. The finger kinematics and the contact informationare then used to filter out unstable grasps. Through many simulatedexamples with three different anthropomorphic hands, wedemonstrate that, compared with previous grasp planners such asthe generic grasp planner in Simox, the proposed grasp plannercan synthesize grasps that are more natural-looking for humans(as measured by the grasp quality measure skewness) for objectswith complex geometries in a short amount of time. Unlike manyother planners, this is achieved without costly model preprocessingsuch as segmentation by parts and medial axis extraction