Data-Driven Grasp Synthesis - A Survey <s> C. Generative Models for Grasp Synthesis <s> Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> C. Generative Models for Grasp Synthesis <s> A major challenge in modeling with BNs is learning the structure from both discrete and multivariate continuous data. A common approach in such situations is to discretize continuous data before structure learning. However efficient methods to discretize high-dimensional variables are largely lacking. This paper presents a novel method specifically aiming at discretization of high-dimensional, high-correlated data. The method consists of two integrated steps: non-linear dimensionality reduction using sparse Gaussian process latent variable models, and discretization by application of a mixture model. The model is fully probabilistic and capable to facilitate structure learning from discretized data, while at the same time retain the continuous representation. We evaluate the effectiveness of the method in the domain of robot grasping. Compared with traditional discretization schemes, our model excels both in task classification and prediction of hand grasp configurations. Further, being a fully probabilistic model it handles uncertainty in the data and can easily be integrated into other frameworks in a principled manner. <s> BIB002
Very little work has been done on learning generative models of the whole grasp process. These kind of approaches identify common structures from a number of examples instead of finding a decision boundary in some feature space or directly comparing to previous examples under some similarity metric. Montesano et al. BIB001 provide one example in which affordances are encoded in terms of an action that is executed on an object and produces a specific effect. The problem of learning a joint distribution over a set of variables is posed as structure learning in a Bayesian network framework. Nodes in this network are formed by object, action and effect features that the robot can observe during execution. Given 300 trials, the robot learns the structure of the Bayesian network. Its validity is demonstrated in an imitation game where the robot observes a human executing one of the known actions on an object and is asked to reproduce the same observed effect when given a new object. Effectively, the robot has to perform inference in the learned network to determine the action with the highest probability to succeed. Song et al. BIB002 approach the problem of inferring a full grasp configuration for an object given a specific task. As in BIB001 , the joint distribution over the set of variables influencing this choice is modeled as a Bayesian network. Additional variables like task, object category and task constraints are introduced. The structure of this model is learned given a large number of grasp examples generated in GraspIt! and annotated with grasp quality metrics as well as suitability for a specific task. The authors exploit non-linear dimensionality reduction techniques to find a discrete representation of continuous variables for efficient and more accurate structure learning. The effectiveness of the method is demonstrated on the synthetic data for different inference tasks. The learned quality of grasps on specific objects given a task is visualized in Fig. 23 . Figure 23 : Ranking of approach vectors for different objects given a specific task. The brighter an area the higher the rank. The darker an area, the lower the rank BIB002 .