Model-free and learning-free grasping by Local Contact Moment matching

This paper addresses the problem of grasping arbitrarily shaped objects, observed as partial point-clouds, without requiring: models of the objects, physics parameters, training data, or other a-priori knowledge. A grasp metric is proposed based on Local Contact Moment (LoCoMo). LoCoMo combines zero-moment shift features, of both hand and object surface patches, to determine local similarity. This metric is then used to search for a set of feasible grasp poses with associated grasp likelihoods. LoCoMo overcomes some limitations of both classical grasp planners and learning-based approaches. Unlike force-closure analysis, LoCoMo does not require knowledge of physical parameters such as friction coefficients, and avoids assumptions about fingertip contacts, instead enabling robust contacts of large areas of hand and object surface. Unlike more recent learning-based approaches, LoCoMo does not require training data, and does not need any prototype grasp configurations to be taught by kinesthetic demonstration. We present results of real-robot experiments grasping 21 different objects, observed by a wrist-mounted depth camera. All objects are grasped successfully when presented to the robot individually. The robot also successfully clears cluttered heaps of objects by sequentially grasping and lifting objects until none remain.</p

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.Comment: IEEE Robotics and Automation Letters. Preprint Version. Accepted February, 202

Integrating Vision and Physical Interaction for Discovery, Segmentation and Grasping of Unknown Objects

In dieser Arbeit werden Verfahren der Bildverarbeitung und die Fähigkeit humanoider Roboter, mit ihrer Umgebung physisch zu interagieren, in engem Zusammenspiel eingesetzt, um unbekannte Objekte zu identifizieren, sie vom Hintergrund und anderen Objekten zu trennen, und letztendlich zu greifen. Im Verlauf dieser interaktiven Exploration werden außerdem Eigenschaften des Objektes wie etwa sein Aussehen und seine Form ermittelt

MULTI-DIGIT HUMAN PREHENSION

The current dissertation addresses the central nervous system (CNS) strategies to solve kinetic redundancy in multi-digit static prehension under different geometries of hand-held objects and systematically varied mechanical constraints such as translation and rotation of the hand-held object. A series of experiments conducted for this dissertation tested the following hypotheses suggested in the current literatures for multi-digit human static prehension: Hierarchical organization hypothesis, principle of superposition hypothesis, proximity hypothesis, and mechanical advantage hypothesis. (1) Forces and moments produced by fingers during circular object prehension were grouped into two independent subsets: one subset related to grasping stability control and the other associated with rotational equilibrium control. This result supports the principle of superposition hypothesis. Individual fingers acted synergistically to compensate each other's errors. This result confirms the hierarchical organization hypothesis in circular object prehension. (2) During fixed object prehension of a rectangular object, the closer the non-task fingers positioned to the task finger, the greater the forces produced by the non-task fingers. However, during free object prehension, the non-task fingers with longer moment arms produced greater forces. The former and latter results support the proximity hypothesis and the mechanical advantage hypothesis, respectively. (3) The grasping stability control and rotational equilibrium control were decoupled during fixed object prehension as well as free object prehension. This result supports the principle of superposition hypothesis regardless of the mechanical constraints provided for these two prehension types. (4) During torque production, the fingers with longer moment arms produced greater forces when the fingers acted as agonists for the torque production. Therefore, the mechanical advantage hypothesis was supported for agonist fingers. (5) Coupling of thumb normal force and virtual finger normal force was not necessitated when horizontal translation of hand-held object was mechanically fixed. However, the coupling of two normal forces was always observed regardless of given translational constraints, and these two normal forces were independent to other mechanical variables such as tangential forces and moments. This result supports the principle of superposition hypothesis in static prehension under varied combinations of translational constraints

The Columbia Grasp Database

Collecting grasp data for learning and benchmarking purposes is very expensive. It would be helpful to have a standard database of graspable objects, along with a set of stable grasps for each object, but no such database exists. In this work we show how to automate the construction of a database consisting of several hands, thousands of objects, and hundreds of thousands of grasps. Using this database, we demonstrate a novel grasp planning algorithm that exploits geometric similarity between a 3D model and the objects in the database to synthesize form closure grasps. Our contributions are this algorithm, and the database itself, which we are releasing to the community as a tool for both grasp planning and benchmarking