A robot can feasibly be given knowledge of a set of tools for manipulation activities (e.g. hammer, knife, spatula). If the robot then operates outside a closed environment it is likely to face situations where the tool it knows is not available, but alternative unknown tools are present. We tackle the problem of finding the best substitute tool based solely on 3D vision data. Our approach has simple hand-coded models of known tools in terms of superquadrics and relationships among them. Our system attempts to fit these models to point clouds of unknown tools, producing a numeric value for how good a fit is. This value can be used to rate candidate substitutes. We explicitly control how closely each part of a tool must match our model, under direction from parameters of a target task. We allow bottom-up information from segmentation to dictate the sizes that should be considered for various parts of the tool. These ideas allow for a flexible matching so that tools may be superficially quite different, but similar in the way that matters. We evaluate our system's ratings relative to other approaches and relative to human performance in the same task. This is an approach to knowledge transfer, via a suitable representation and reasoning engine, and we discuss how this could be extended to transfer in planning.

Abstract-Robots performing everyday tasks such as cooking in a kitchen need to be able to deal with variations in the household tools that may be available. Given a particular task and a set of tools available, the robot needs to be able to assess which would be the best tool for the task, and also where to grasp that tool and how to orient it. This requires an understanding of what is important in a tool for a given task, and how the grasping and orientation relate to performance in the task. A robot can learn this by trying out many examples. This learning can be faster if these trials are done in simulation using tool models acquired from the Web. We provide a semi-automatic pipeline to process 3D models from the Web, allowing us to train from many different tools and their uses in simulation. We represent a tool object and its grasp and orientation using 21 parameters which capture the shapes and sizes of principal parts and the relationships among them. We then learn a 'task function' that maps this 21 parameter vector to a value describing how effective it is for a particular task. Our trained system can then process the unsegmented point cloud of a new tool and output a score and a way of using the tool for a particular task. We compare our approach with the closest one in the literature and show that we achieve significantly better results.

In this paper, we propose a temporal planning model for real-time generation of narratives in Interactive Storytelling systems. The model takes into account continuous branched time and the specification of constraints defined as temporal formulae over dramatic properties of the narrative (e.g. joy or tension). In order to address real-time generation, dramatic properties are modeled as varying linearly and events go through a preprocessing stage. As proof of concept, the model is incorporated into an existing storytelling system, LOGTELL, which provides a way to logically specify genres; allows user interaction to influence events in the unrolling narrative; and dramatizes the story in a 3D computer graphics world. To illustrate the generation of narratives, we present a simple narrative example in the Swords and Dragons genre.