Object Recall from NaturalLanguage Descriptions for Autonomous Robotic Grasping Humans acquire grasping skills through repeated interaction with the objects; and they internalize the knowledge of various physical attributes of such objects. Even blindfolded, humans can reasonably estimate a suitable grasp pose given only the objectxe2x80x99s description. Human brain relies on the knowledge of having seen such objects and recalling its physical features such as shape, size, weight to estimate feasible grasps. In such a scenario, knowledge of an objectxe2x80x99s features is key to executing a successful grasp. This paper aims to provide this xe2x80x99recallxe2x80x99 ability to robots by proposing a methodology to represent human memory of objects with a dataset of objects and their physical features. A joint probability distance metric is derived, which can match the natural language descriptions to reference object features so as to recall and identify a particular object from the reference dataset, thereby facilitating better grasp planning. Experiments were performed to evaluate the accuracy of object recall, and simulation of an anthropomorphic robot hand was conducted for object grasping based on the recalled object features. The experiment results showed the accuracy of the proposed metric and effectiveness in object grasping.

Recalling Candidates of Grasping Method from an Object Image using Neural Network Robots are required to support peoplexe2x80x99s work. In order to alleviate the burden on people, it is desirable that robot can automatically generate and execute complicated motions according to simple directions from people. However, there are multiple grasping methods for one object. In order to select a motion suitable for the direction, it is important to estimate candidates of grasping method for the object. In this research, we purpose to recall candidates of grasping position and hand shape from an object image. In learning, a network that outputs a plurality of grasping method candidates for one object image to each channel of a multichannel image is used. At this time, a plurality of grasping methods are not learned at same time, learned one by one. The similar grasping method for the similar object shape is automatically clustered to each output channel in the learning process, and a grasping method having a characteristic difference is presented as a candidate. We show the usefulness of this method using experimental examples.