Learning Grasp Strategies Composed of Contact Relative Motions

Of central importance to grasp synthesis algorithms are the assumptions made about the object to be grasped and the sensory information that is available. Many approaches avoid the issue of sensing entirely by assuming that complete information is available. In contrast, this paper proposes an approach to grasp synthesis expressed in terms of units of control that simultaneously change the contact configuration and sense information about the object and the relative manipulator-object pose. These units of control, known as contact relative motions (CRMs), allow the grasp synthesis problem to be recast as an optimal control problem where the goal is to find a strategy for executing CRMs that leads to a grasp in the shortest number of steps. An experiment is described that uses Robonaut, the NASA-JSC space humanoid, to show that CRMs are a viable means of synthesizing grasps. However, because of the limited amount of information that a single CRM can sense, the optimal control problem may be partially observable. This paper proposes expressing the problem as a k-order Markov Decision Process (MDP) and solving it using Reinforcement Learning. This approach is tested in a simulation of a two-contact manipulator that learns to grasp an object. Grasp strategies learned in simulation are tested on the physical Robonaut platform and found to lead to grasp configurations consistently

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

Learning Grasp Strategies Composed of Contact Relative Motions

Abstract — Of central importance to grasp synthesis algorithms are the assumptions made about the object to be grasped and the sensory information that is available. Many approaches avoid the issue of sensing entirely by assuming that complete information is available. In contrast, this paper focuses on the case where force feedback is the only source of new information and limited prior information is available. Although, in general, visual information is also available, the emphasis on force feedback allows this paper to focus on the partially observable nature of the grasp synthesis problem. In order to investigate this question, this paper introduces a parameterizable space of atomic units of control known as contact relative motions (CRMs). CRMs simultaneously displace contacts on the object surface and gather force feedback information relevant to the object shape and the relative manipulator-object pose. This allows the grasp synthesis problem to be re-cast as an optimal control problem where the goal is to find a strategy for executing CRMs that leads to a grasp in the shortest number of steps. Since local force feedback information usually does not completely determine system state, the control problem is partially observable. This paper expresses the partially observable problem as a k-order Markov Decision Process (MDP) and solves it using Reinforcement Learning. Although this approach can be expected to extend to the grasping of spatial objects, this paper focuses on the case of grasping planar objects in order to explore the ideas. The approach is tested in planar simulation and is demonstrated to work in practice using Robonaut, the NASA-JSC space humanoid. I

Haptische Exploration von unbekannten Objekten mit einer humanoiden Roboterhand

In dieser Arbeit wurden Methoden und Anwendungen der autonomen, haptischen Exploration von unbekannten Objekten mit einer humanoiden Roboterhand untersucht. Es wurde ein Explorationsverfahren entwickelt, mit dem ein Roboter haptische Objektmerkmale erfassen kann. Als wichtige Anwendungen wurde die Planung von möglichen Griffen auf Grundlage der Explorationsdaten untersucht, sowie eine zur Klassifizierung und Erkennung geeignete Objektrepräsentation