Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an open environment would benefit from the ability to learn it through their interaction with their environment. This ability furthermore opens the way to the acquisition of affordances maps in which the action capabilities of the robot structure its visual scene understanding. We propose an approach to build such affordances maps by relying on an interactive perception approach and an online classification. In the proposed formalization of affordances, actions and effects are related to visual features, not objects, and they can be combined. We have tested the approach on three action primitives and on a real PR2 robot.
In a more developmental perspective, @cite_2 proposed a framework to learn composite affordances by starting from low level affordances. Their approach is split into 3 steps: first, the robot explores its environment with a reactive behavior, like a grasp reflex, and collects visual data consisting of SIFT. Then, in a second step, basic affordances are learned with simple actions such as pushing or gripping. Finally, in the third step, the robot learns composite affordances based on a combination of the basic action used in the previous step. For instance, this combination of actions allows the robot to achieve stacking. They validate their framework with a mobile robot equipped with a stereo camera and a magnetized end-effector. In a real environment the robot tries to learn to identify objects that are liftable with its magnetized end-effector.
@cite_2
2091938290 2099816052 2155217025 1989695841
A fundamental requirement of any autonomous robot system is the ability to predict the affordances of its environment. The set of affordances define the actions that are available to the agent given the robotâs context. A standard approach to affordance learning is direct perception, which learns direct mappings from sensor measurements to affordance labels. For example, a robot designed for cross-country navigation could map stereo depth information and image features directly into predictions about the traversability of terrain regions. While this approach can succeed for a small number of affordances, it does not scale well as the number of affordances increases. In this paper, we show that visual object categories can be used as an intermediate representation that makes the affordance learning problem scalable. We develop a probabilistic graphical model which we call the CategoryâAffordance (CA) model, which describes the relationships between object categories, affordances, and appearance. This model casts visual object categorization as an intermediate inference step in affordance prediction. We describe several novel affordance learning and training strategies that are supported by our new model. Experimental results with indoor mobile robots evaluate these different strategies and demonstrate the advantages of the CA model in affordance learning, especially when learning from limited size data sets. The concept of affordances, as proposed by J.J. Gibson, refers to the relationship between the organism and its environment and has become popular in autonomous robot control. The learning of affordances in autonomous robots, however, typically requires a large set of training data obtained from the interactions of the robot with its environment. Therefore, the learning process is not only time-consuming, and costly but is also risky since some of the interactions may inflict damage on the robot. In this paper, we study the learning of traversability affordance on a mobile robot and investigate how the number of interactions required can be minimized with minimial degradation on the learning process. Specifically, we propose a two step learning process which consists of bootstrapping and curiosity-based learning phases. In the bootstrapping phase, a small set of initial interaction data are used to find the relevant perceptual features for the affordance, and a support vector machine (SVM) classifier is trained. In the curiosity-driven learning phase, a curiosity band around the decision hyperplane of the SVM is used to decide whether a given interaction opportunity is worth exploring or not. Specifically, if the output of the SVM for a given percept lies within curiosity band, indicating that the classifier is not so certain about the hypothesized effect of the interaction, the robot goes ahead with the interaction, and skips if not. Our studies within a physics-based robot simulator show that the robot can achieve better learning with the proposed curiosity-driven learning method for a fixed number of interactions. The results also show that, for optimum performance, there exists a minimum number of initial interactions to be used for bootstrapping. Finally, the trained classifier with the proposed learning method was also successfully tested on the real robot. 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. The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions 0-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are not directly perceivable increase the usefulness of robotic agents with manipulation capabilities, so that by appropriate manipulation they can modify the object configuration until the seeked affordance becomes available. In this paper we show how 0-order affordances depending on the geometry of the objects and their pose can be learned using a supervised learning strategy on 3D mesh representations of the objects allowing the use of the whole object geometry. Moreover, we show how the learned affordances can be detected in real scenes obtained with a low-cost depth sensor like the Microsoft Kinect through object recognition and 6D0F pose estimation and present results for both learning on meshes and detection on real scenes to demonstrate the practical application of the presented approach.